How do I start data science - 2023-06-27T102319.767

What is Data Governance and why is it important?

Introduction:

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In the age of information, organizations face unprecedented challenges in managing and leveraging the vast amount of data at their disposal. To ensure data is properly managed, controlled, and utilized to its full potential, data governance emerges as a critical discipline. Data governance encompasses a set of processes, policies, and frameworks that define how organizations manage, protect, and utilize their data assets. This article explores the concept of data governance, its key components, and highlights its importance in today’s data-driven landscape.

Understanding Data Governance A. Definition and Purpose:

Data governance refers to the overall management framework and processes that govern the availability, integrity, usability, and security of data within an organization. It establishes accountability, defines roles and responsibilities, and ensures compliance with regulatory requirements and organizational policies.

B. Key Components of Data Governance:

Data Strategy: The overarching vision and objectives that guide data governance initiatives, aligning data management practices with business goals.

Data Policies and Standards: A set of rules and guidelines that outline how data should be handled, stored, accessed, and protected.

Data Stewardship: The designation of individuals or teams responsible for data quality, integrity, and compliance within specific data domains.

Data Lifecycle Management: The management of data throughout its lifecycle, from creation to archival or disposal, including data retention policies.

Data Quality Management: Processes and controls to ensure the accuracy, completeness, consistency, and reliability of data.

Metadata Management: The management of metadata, which provides essential context and information about the data, such as its origin, structure, and meaning.

The Importance of Data Governance A. Data Integrity and Trustworthiness:

Data governance ensures that data is accurate, consistent, and reliable. By establishing data quality standards, validation processes, and data stewardship roles, organizations can have confidence in the integrity of their data, leading to better decision-making and improved operational efficiency.

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B. Regulatory Compliance and Risk Mitigation: Data governance enables organizations to comply with data protection regulations, industry standards, and legal requirements. It ensures proper data handling, privacy protection, and safeguards against data breaches, reducing the risk of legal and financial repercussions.

C. Data Accountability and Ownership: Data governance establishes clear ownership and accountability for data assets. By assigning data stewards and defining roles and responsibilities, organizations ensure that data is properly managed, maintained, and protected throughout its lifecycle.

D. Improved Decision-Making: Effective data governance provides a trusted and reliable foundation for decision-making processes. By ensuring data accuracy, consistency, and availability, organizations can make informed decisions based on reliable insights, leading to better business outcomes.

E. Data Collaboration and Consistency: Data governance promotes data collaboration and consistency across different departments and business units. It breaks down data silos, encourages data sharing, and establishes a common understanding of data definitions, facilitating seamless data integration and analysis.

F. Data Monetization and Value Creation: Properly governed data is a valuable asset that can be monetized and leveraged for strategic initiatives. Data governance establishes data as a tangible business asset, enabling organizations to explore data-driven business models, gain competitive advantage, and uncover new revenue streams.

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G. Customer Trust and Experience: Data governance plays a crucial role in building and maintaining customer trust. By safeguarding customer data, ensuring privacy protection, and implementing transparent data practices, organizations can enhance customer confidence, loyalty, and deliver personalized experiences.

Implementing Effective Data Governance A. Leadership and Executive Sponsorship:

Data governance requires strong leadership and executive sponsorship to drive organizational commitment, allocate resources, and prioritize data governance initiatives.

B. Data Governance Framework: Developing a comprehensive data governance framework that aligns with organizational goals and objectives is essential. The framework should include policies, procedures, and guidelines for data management, privacy, security, and compliance.

C. Data Stewardship and Data Management Roles: Identifying data stewards and data management roles within the organization is critical for effective data governance. These roles should be responsible for data quality, data integrity, data access, and compliance.

D. Data Governance Policies and Standards: Establishing clear and comprehensive data governance policies and standards is essential to ensure consistency and compliance. These policies should address data quality, data privacy, data security, data sharing, and data retention.

E. Data Governance Tools and Technologies: Leveraging data governance tools and technologies can streamline data governance processes, automate data management tasks, and provide visibility into data assets. These tools can include data cataloging, metadata management, data lineage, and data security solutions.

F. Continuous Monitoring and Improvement: Data governance is an ongoing process that requires continuous monitoring, evaluation, and improvement. Regular audits, performance metrics, and feedback loops should be established to ensure the effectiveness and efficiency of data governance practices.

Overcoming Challenges in Data Governance A. Cultural Shift and Change Management:

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Implementing data governance may require a cultural shift within the organization. Proper change management practices, communication, and training programs are necessary to ensure buy-in and adoption at all levels.


B. Data Silos and Integration: Data governance should address the challenge of data silos by promoting data integration, collaboration, and standardized data practices across the organization. Integration of disparate data sources and systems is crucial for comprehensive data governance.

C. Data Privacy and Security: With the increasing focus on data privacy and security, data governance should incorporate robust practices to protect sensitive data. This includes implementing encryption, access controls, data classification, and monitoring mechanisms.

D. Data Governance Maturity: Organizations may face challenges in establishing a mature data governance program. It requires a phased approach, starting with foundational elements and gradually evolving towards advanced data governance practices.

Future Trends in Data Governance A. AI and Automation:

Artificial Intelligence (AI) and automation technologies will play a significant role in data governance, enabling intelligent data management, automated compliance, and proactive data quality management.

B. Ethical Data Governance: Ethics in data governance will gain prominence, focusing on responsible data usage, privacy protection, and transparency in data practices. Organizations will prioritize ethical considerations and align data governance with ethical frameworks.

C. Data Governance for Unstructured Data: As the volume of unstructured data, such as text, images, and videos, continues to grow, data governance will need to address the challenges of managing and governing these types of data effectively.

D. Blockchain Technology: Blockchain technology holds potential for enhancing data governance by providing decentralized, transparent, and tamper-proof data storage, verification, and access control mechanisms.

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Conclusion:

Data governance is no longer a luxury but a necessity for organizations seeking to maximize the value of their data assets. It establishes the framework, policies, and processes that ensure data is properly managed, protected, and utilized. By implementing effective data governance practices, organizations can achieve data integrity, compliance with regulations, improved decision-making, and enhanced customer experiences. As the data landscape continues to evolve, embracing emerging trends and technologies in data governance will be crucial for organizations to adapt, thrive, and unlock the full potential of their data-driven endeavors. With a solid foundation of data governance, organizations can confidently navigate the complexities of the data-driven world and pave the way for innovation and sustainable growth.

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How do I start data science (5)

Is data science is better than MBA?

Data science and Master of Business Administration (MBA) are two popular career paths that offer unique opportunities and challenges. In this article, we will compare data science and MBA, exploring their respective strengths, career prospects, and potential for professional growth. By examining the key aspects of each field, we aim to provide insights that can help individuals make informed decisions about their career paths and understand the factors that differentiate these two options.

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Understanding Data Science:

Data science is an interdisciplinary field that combines statistical analysis, machine learning, programming, and domain knowledge to extract insights and make data-driven decisions. Data scientists work with large datasets, employ advanced algorithms, and utilize programming languages like Python or R to derive valuable insights. They are in high demand across various industries, including technology, finance, healthcare, and marketing, due to the increasing reliance on data-driven strategies.

Exploring the MBA Degree: An MBA is a postgraduate degree that focuses on developing management and business skills. The curriculum typically covers areas such as finance, marketing, operations, strategy, and leadership. MBA graduates gain a broad understanding of business principles and management strategies. The degree is highly valued in the corporate world, offering opportunities for leadership roles, entrepreneurship, and networking with industry professionals.

Career Opportunities and Growth:

a. Data Science: The demand for data scientists is rapidly growing across industries. Data scientists can work as data analysts, machine learning engineers, data engineers, or AI specialists. They have the opportunity to solve complex problems, analyze vast amounts of data, and provide insights that drive strategic decision-making. The field offers high salaries, career advancement prospects, and the ability to work on cutting-edge technologies. Data science also provides opportunities for entrepreneurship and innovation, as data-driven startups continue to emerge.

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b. MBA: An MBA opens doors to various career paths in business and management. Graduates can pursue roles in finance, consulting, marketing, operations, and general management. MBA holders often aim for executive positions and leadership roles in organizations. The degree equips individuals with business acumen, strategic thinking, and networking skills. An MBA is particularly valuable for those interested in entrepreneurship or seeking to transition to management roles within their industry.

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Skill Sets and Specializations:

a. Data Science: Data scientists require a strong foundation in programming, statistics, and machine learning. They should be proficient in programming languages like Python or R and possess expertise in data manipulation, statistical analysis, and data visualization. Specializations within data science include natural language processing, computer vision, and big data analytics. Data scientists need to continuously update their skills to stay current with evolving technologies and techniques.

b. MBA: MBA programs focus on developing a range of skills, including strategic thinking, problem-solving, leadership, finance, marketing, and communication. MBA graduates gain a broad understanding of business operations and management principles. They often specialize in areas such as finance, marketing, entrepreneurship, or consulting, depending on their career goals and interests. MBA programs also emphasize soft skills such as teamwork, negotiation, and presentation abilities.

Industry Trends and Job Market: a. Data Science: Data science is a rapidly expanding field, with an increasing demand for skilled professionals. Industries such as technology, finance, healthcare, e-commerce, and marketing heavily rely on data analysis and insights for decision-making. The job market for data scientists is competitive but offers a wide range of opportunities.

b. MBA: The MBA job market is diverse, with opportunities in various industries, including consulting firms, multinational corporations, startups, and non-profit organizations. However, the job market for MBA graduates can be influenced by economic conditions and industry trends. Certain industries such as finance and consulting have traditionally valued the MBA degree, while others may prioritize industry-specific experience or technical expertise.

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Return on Investment:

a. Data Science: Data science roles often come with competitive salaries due to the high demand for skilled professionals. Data scientists have the potential to earn substantial income, especially as they gain experience and specialize in niche areas. The return on investment in terms of time and financial resources spent on acquiring data science skills can be significant.

b. MBA: An MBA degree can also yield a strong return on investment, particularly for individuals aiming for leadership positions or career transitions. MBA graduates, on average, tend to earn higher salaries compared to their pre-MBA earnings. However, it is important to consider the cost of pursuing an MBA, including tuition fees and the opportunity cost of leaving the workforce for a full-time program.

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Personal Interests and Career Alignment:

Choosing between data science and an MBA should also consider personal interests, long-term goals, and alignment with career aspirations. Factors to consider include: a. Passion for Data and Technology: Data science is ideal for individuals who have a strong interest in working with data, programming, and advanced analytics. If you enjoy exploring patterns, uncovering insights, and leveraging technology to solve complex problems, data science may be a better fit. b. Business Management and Strategy: If your passion lies in strategic thinking, decision-making, and leading organizations, an MBA may be more suitable. The program offers a comprehensive understanding of business principles, management strategies, and networking opportunities within the corporate world. c. Industry Preferences: Consider the industries you are interested in and the skills in demand within those sectors. Data science has broad applicability across industries, while an MBA may be particularly valuable in sectors such as finance, consulting, or entrepreneurship.

Conclusion:

Choosing between data science and an MBA depends on individual preferences, career goals, and the desired skill set. Data science offers opportunities in a rapidly growing field with high demand and rewarding career prospects. On the other hand, an MBA provides a comprehensive business education, leadership skills, and networking opportunities within the corporate world. Ultimately, it is crucial to consider personal interests, long-term aspirations, and the specific requirements and trends of the industry to make an informed decision about which path to pursue.

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Gradient Boosting in Data Scientist Courses in Bangalore

Gradient Boosting in Data Scientist Courses in Bangalore

Introduction:

With huge and complicated datasets, gradient boosting is a strategy that stands out for its prediction speed and accuracy. This method has delivered the greatest results across a range of applications, including Kaggle competitions and commercial machine-learning solutions. Errors are a significant factor in every machine learning system, as we already know. Bias error and variance error are the two basic categories of error. The gradient boost approach aids in reducing the model’s bias inaccuracy.

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Before delving into the specifics of this algorithm, it is important to understand the AdaBoost Algorithm, another boosting technique. This algorithm begins by creating a decision stump, after which all the data points are given equal weights. The weights for all the incorrectly classified points are then increased, while those that are simple to classify or are correctly classified have their weights decreased. These weighted data points are given their own decision stump. The purpose of this is to enhance the first stump’s forecasts. 

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The primary distinction between these two techniques is that Gradient boosting uses Decision Trees as its fixed base estimator, whereas AdaBoost allows us to customize the base estimator to meet our specific needs. Gradient boosting is a method for gradually, additively, and sequentially training multiple models. The way that AdaBoost and Gradient Boosting Algorithms uncover the flaws of weak learners (such as decision trees), is the main distinction between the two techniques. 

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Gradient boosting accomplishes the same task as the AdaBoost model, which detects flaws by employing high-weight data points (y=ax+b+e, where e deserves special attention as the error term). The loss function is a metric used to assess how well the coefficients of the model fit the underlying data. Depending on the goal of our optimization, a logical grasp of the loss function would be necessary. 

The loss function, for instance, would be based on the discrepancy between actual and expected house prices if we were using a regression to try and predict sales prices. Similarly, the loss function would be a gauge of how well our prediction model categorizes subprime loans if we aimed to categorize credit defaults. The ability to optimize a user-specified cost function rather than a loss function, which typically offers less control and does not closely resemble real-world applications, is one of the main reasons to utilize gradient boosting.

What is gradient boosting?

The primary concept underlying this algorithm is to build models in succession while attempting to minimize the flaws of the prior model. But how do we go about that? How can the error be minimized? By basing a new model on the errors or residuals of the prior one, this is accomplished.

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Gradient Boosting Regressor is used when the target column is continuous; Gradient Boosting Classifier is used when the problem is one of classification. Between the two, the “Loss function” is the only distinction. The goal is to use gradient descent to add weak learners and reduce this loss function. Since it is based on a loss function, we will have various loss functions for regression problems, such as Mean Squared Error (MSE), and for classification problems, such as log-likelihood.

Why use gradient boosting?

Gradient boosting’s benefits

High forecast accuracy: Although adopting gradient boosting doesn’t have this benefit, historically speaking, gradient boosting has frequently won different Kaggle events. This may be attributed to the features of gradient boosting, which combine numerous smaller models and rely on collective intelligence to make the final prediction rather than attempting to fit all the data patterns into a single model.

Flexibility: Using different weak learners (not only decision trees), loss functions, and data types (ordinal, continuous, categorical, etc.), gradient boosting is a form of model that can be used for regression or classification problems.

Easily accessible: Whether you are using R (gbm, xgboost, lightgbm), Julia (GradientBoost), or Python (sklearn, xgboost, lightgbm, catboost), there are a variety of modules from which you may apply gradient boosting to your data problem.

Interpretability: When compared to model topologies like neural networks, gradient-boosting machines probably strike a better compromise between complexity and interpretability. Here is a blog post where I go into great detail about how SHAP functions in case you want to read more about interpreting machine learning models in general.

Gradient Boosting Courses in Bangalore

A reputable and well-known institution offering data science courses in Bangalore is 360DigitMG. The full data science life cycle is thoroughly covered in their course offerings. They were founded in 2013 and currently provide excellent courses in a variety of subjects through 7 centers worldwide.

Their best data science courses include, 

Course Syllabus

  1. CRISP-DM – Project Management Methodology
  2. Exploratory Data Analytics (EDA) / Descriptive Analytics or Descriptive Statistics
  3. Statistical Data Business Intelligence and Data Visualization
  4. Plots & Inferential Statistics
  5. Probability Distributions (Continuous & Discrete)
  6. Hypothesis Testing – The ‘4’ Must Know Hypothesis Tests
  7. Data Mining Supervised Learning – Linear Regression, OLS
  8. Predictive Modelling – Multiple Linear Regression
  9. Lasso and Ridge Regressions
  10. Logistic Regression – Binary Value Prediction, MLE
  11. Multinomial Regression and Ordinal Regression & more 

They include training modules for each special technique and have included one for boosting too. Learn about using ensemble approaches to enhance the consistency and precision of decision tree models. In ensemble techniques, bagging and boosting are the go-to methods. In this module, we talk about how Bagging and Boosting methods use parallel and sequential approaches. 

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The outcome of Random Forest, another ensemble technique built utilizing several Decision Trees, is derived by aggregating the findings from these combinations of trees. This section continues with a discussion of the boosting techniques AdaBoost and Extreme Gradient Boosting. Additionally, stacking techniques will be covered. Learn about the algorithms used by many aspiring data scientists to place first in contests like Kaggle, CrowdAnalytix, etc., and to provide unmatched accuracy.

  • Overfitting
  • Underfitting
  • Voting
  • Stacking
  • Bagging
  • Random Forest
  • Boosting
  • AdaBoost / Adaptive Boosting Algorithm
  • Checking for Underfitting and Overfitting in AdaBoost
  • Generalization and Regulation Techniques to avoid overfitting in AdaBoost
  • Gradient Boosting Algorithm
  • Checking for Underfitting and Overfitting in Gradient Boosting
  • Generalization and Regulation Techniques to avoid overfitting in Gradient Boosting
  • Extreme Gradient Boosting (XGB) Algorithm
  • Checking for Underfitting and Overfitting in XGB
  • Generalization and Regulation Techniques to avoid overfitting in XGB

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Git in Data Science Institutes in Bangalore

Git in Data Science Institutes in Bangalore

Introduction:

Version control is an essential and popular concept that is required in the day-to-day task of a data science professional. Newcomers and beginners who want to make a career in data science and data analytics should know about version control.

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Unfortunately, in the competitive job market of data science, there is a steep shortage of talented and certified data scientists with complete knowledge of version control systems.

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To make a successful career in data science, you need to navigate through popular concepts like Git and GitHub. A thorough understanding of Git and GitHub will help professionals to erase all the problems that arise while working independently and remotely as a team.

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Benefits of undergoing a data science course with Git in Bangalore

Data science aspirants can learn all the ins and outs of Git by enrolling in a comprehensive data science course curriculum in Bangalore. Novice data science professionals understand Git and even use Git tools and techniques to make their data science projects more straightforward and easy to track. After the data science course competition in Bangalore, you can add valuable data scientist skills to your portfolio. A data science training institution in Bangalore introduces data science learners to version control concepts with the help of Git. Data science professionals have to use Git on their projects for tracking files, comparing differences, saving files, modifying or undoing changes, and enabling them to create new repositories.

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Reasons for learning Git

The following are the reasons that state why data scientists in the present day should learn skills and knowledge about Git. 

  • Acquiring knowledge about Git will help data science professionals perform collaborative work at large and small data science teams.
  • To track the code or any modifications made in the file
  • To build a personal data science project portfolio on repository hosting platforms like GitLab or GitHub
  • For learning and contributing from an open-source project

What is Git?

A data science project requires the collaboration of many people, such as

  • Researchers
  • Artificial intelligence professionals
  • Software developers
  • Data scientists
  • Developers
  • Testers to refine a code base.

Git is a popular command-line version control system designed to track changes over a particular period. This version control enables developers to record all the changes taking place and mend them into one repository. This article will help you to know various details about GitHub that are required for data science projects and why acquiring knowledge about GitHub is essential in the learning path of a data science beginner.

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How does Git work?

Git is a command line or distributed version control system that tracks source code changes during software development. This version control system is used for coding and collaboration platforms to enable an easy flow of work among different team members. GitHub is a popular web hosting platform that hosts Git commands and enables you to get a copy of your task in case the local repository of your system cracks up or is lost. 

What are the best and most effective practices for structuring data science projects with the help of git?

To execute a data science project, professionals require a versioning system for tracking changes and making the project more systematic. It resonates with easy and quick collaboration among team members. For tracking all the changes and modifications made in the data science project, both Git as well as GitHub can be used by the professional. This enables managers and developers to review the modifications and review the project file’s existing changes. Some of the best practices while using Git and GitHub are as follows

  • Keeping track of versions and changes of projects locally with the help of Git
  • Keeping all the project files in a single place
  • Performing analytics and storing ML models using different tools like Tableau or through code 

What are the basic Git commands that every data scientist should know? 

Knowing the basic Git commands is essential to help data scientists work with repositories. There are several Git commands available today, and you need to know about the commands. Following are the top Git commands that data scientists normally use in their day-to-day activities.

  • Git init

If you’re beginning a new data science project in the GitHub repository, then you need to use this command. 

  • Git clone

You can run this command to download existing code from the remote repositories; this command is used for making identical copies of the current version of the project located in the repository and then saving the version to the local working environment.

  • Git branch

Developers use the Git branch command for working on one project simultaneously. This command helps in creating, listing, and deleting branches.

  • Git status

The Git status command is used for getting all the updates and information about the current branch.

  • Git add

All the changes made can be included in a file with the help of this command, like creating, modifying, and deleting before committing. Changes will not be saved unless you use the git commit command.

  • Git commit

You can set a checkpoint in your development and save the modifications locally with the help of this command.

  • Git push

The git push command can be used after committing the changes and for sending the changes to the remote server. For example, all the comments will be uploaded to the remote repository with the help of this command.

  • Git pull

You can get all the updates from a remote repository using this command. Then, this command is run to get updates from the repository.

Why do data scientists need to know about GitHub?

Data science professionals working on a particular project might make some changes at the last moment to their code. Data scientists can bring improvements or changes to their code, and in order to avoid any confusion or errors, they can merge the modification using GitHub. GitHub is a popular version control system. Data scientists require knowledge of Git and GitHub for sourcing code management. 

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Conclusion

As aspiring data scientists, professionals must understand the fundamentals and concepts of using GitHub and Git to complete data science projects. Many organizations and companies use agile development approaches and methods and Git tools for tracking the changes made on the projects. To learn and polish your skills about Git for data science projects, you can enroll in a data science bootcamp where you can gain insights about GitHub data science and learn a great variety of details about Git tools along with their functionality.

A complete overview of data science

A complete overview of data science

What is data science? 

An overview of data science that helps in extracting useful insights and knowledge from huge sets of unstructured and structured data using mathematical algorithms and scientific inference is known as data science. Data science includes a combination of computer science, statistical mathematics, data analysis, machine learning, domain knowledge, and data visualization. These mathematical algorithms require lots of processing and are implemented using computer programs and run on hardware. 

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Application of data science

Following are the application of data science that depends on data science and ML technologies to solve certain problem sets.  Are you looking to become a Data Scientist? Go through 360DigiTMG’s PG Diploma in Data Science and Artificial Intelligence!

Internet search results 

When a person searches on the internet or Google, critical ML algorithms decide the most relevant search results. Using these ML algorithms, the pages can be ranked accordingly so that the person gets the most relevant page just by clicking a button. 

Recommendation engine

The recommendation engine is a great example of a data science application. It is used by the music streaming service Spotify to recommend its user’s music lists as per their taste and preference. Data science algorithms generate data of each user over some time and recommend the music of their taste. This helps companies to attract more customers. Earn yourself a promising career in data science by enrolling in the Data Science Certification in Chennai offered by 360DigiTMG.

Intelligent digital assistant

One application of data science is using advanced machine learning algorithms in text or voice-based digital assistants, which are also called chatbots. For instance, Google assistant uses ML algorithms to translate the user’s speech to text, know about the context and provide relevant information. 

Addition to the field of data science

Data science has been evolving since the early 200s and includes more technologies in this multidisciplinary field. Some recent additions include-

Artificial intelligence

One core component of data science is known to be machine learning. With the rapid parallel computing capabilities, the latest addition to the field of data science is known to be deep learning.  Also, check the Best Data Science Institute in Bangalore with Placement to start a career in Data Science.

Intelligent systems 

Data science has included data-driven smart apps and intelligent applications accessible in portable form. A significant portion of data science is centered around machine learning which is the basis of these intelligent systems and smart apps. 

Edge computing 

This computing concept is related to the Internet of Things and has been recently incorporated into this field. It includes putting data science pipeline of data processing, collection, and delivery. This recently developed concept is availed through IoT. Become a Data Scientist with 360DigiTMG Data Science Coaching in Hyderabad. Get trained by the alumni from IIT, IIM, and ISB.

Security

One main challenge in the field of digital space is security. Digital systems are more vulnerable to hacking and malware injection concepts. Data science has recently added technologies and advancements to prevent the vulnerability of the digital system. Machine learning algorithms can now detect malware and computer viruses more effectively than traditional ML algorithms.

The life cycle of data science

Project analysis – the first step of data science is to determine the needs of the project regarding availability, source, the budget of data 

Data preparation: Here, raw data is converted to structured data and includes data visualization, data cleaning, transformation, etc. 

Exploratory data analysis- data scientist uses different tools and programming languages to explore data and extract insights. It includes feature selection, visualization, model selection, and rapid prototyping.

Model building – this step model is developed using ML technologies and an ideal hyperparameter. 

Deployment – after building the model, the next step is to deploy it in the real world in mobile or web apps. 

Real-world testing and results-Model output is monitored to detect where the output is failing. The key findings are noted and informed to stakeholders.

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360DigiTMG – Data Analytics, Data Science Course Training Hyderabad

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What are the different types of data science certification?

What Are The Different Types of Data Science Certification

The data science certification curriculum prepares you for every job prospect. Data science certification can be both an online and offline skill assessment certificate that makes you industry-ready in front of the company that hires you to obtain meaningful data-driven insights. Data science is important because of the growing importance of data visualization, data analysis, and machine learning. Earn yourself a promising career in data science by enrolling in the Data Science Course in Pune offered by 360DigiTMG.

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Top data science certification

Data science offers a lucrative package. To get recruited by a top IT company, you need to boost your resume with the help of data science programs that come in the form of certification and help you earn the standard market accordance data science skills. Different varieties of data science certifications help you go the extra mile to boost your performance and credibility in your portfolio. This data science helps in career transitioning and lets you earn career growth opportunities and perks. Looking forward to becoming a Data Scientist? Check out the Data Science Course and get certified today.

Dell EMC Proven Professional Certification Program

It offers comprehensive learning, practitioner, hands-on learning, and certification program. You can be a professional data scientist as soon as you pass this data science exam. Passing this certification would mean to your recruiters that you have possessed all the necessary skills. It offers specialist level and associate level programs considered to be advanced certification. The expiration of this certification is valid for up to two years, and the cost incurred to earn this certification is $200.

SAS Academy for Data Science

There are three programs included in this data science certification. They are-

  • A program that emphasizes big data skills
  • A program that emphasizes data analysis skills
  • A program that focuses on big data as well as data analytics skills

In this data science certifications, you get both the experience and the exposure to using data science tools and earn this certification. This credential certification doesn’t expire, and the minimum price incurred is $2250 for an individual certification course. Also, check this Data Science Chennai to start a career in Data Science.

Google professional data engineer certification

Google offers this kind of data science certification which helps candidates, especially those who want to design and monitor data processing systems and are interested in gaining fundamental knowledge about ML models. The cost of this certification is $200 and helps you learn Cloud SQL, Dataproc to transfer your present workplace to the cloud. It is a two-hour certification examination that sets you up for operationalizing, designing, monitoring, and building data processing system or other ML models. It helps you to learn quality industry-level experience.

Certified Analytics Professional

A vendor-neutral data scientist is offered by a certified analytics professional (cap) earning this certification would mean that you are not biased towards particular software. It also shows your recruiters that you have extensive knowledge in this domain. The expiration of this certification is valid for up to three years. Become a Data Scientist with 360DigiTMG Data Science Coaching in Bangalore. Get trained by the alumni from IIT, IIM, and ISB.

BM Data Science Professional Certification 

This certification helps candidates become job-ready and learn trendy data science tools and additional features, having beginner-level knowledge of deep learning algorithms, machine learning, and several data visualization techniques. Earning this certification helps you to learn practical skills in an industry-relevant way. You get real-time data solutions and unlimited access to all ten courses at 39 US dollars.

Want to learn more about data science? Enroll in the Data Science Training institute in Hyderabad do so.

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360DigiTMG – Data Analytics, Data Science Course Training Hyderabad
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+919989994319

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Artificial Intelligence and Machine Learning Basics

Artificial Intelligence and Machine Learning Basics

What Do We Understand by Artificial Intelligence?

Artificial intelligence refers to the engineering and science of developing intelligent machines that can perform and exhibit cognitive functioning like humans. It consists of learning, reasoning, perceiving, and solving capabilities and abilities. The benchmark of intelligence for artificial intelligence stands at the human standard. It relates to factors and parameters such as vision, speech, and thinking. 

AI does not confine itself to the methods and practices that can get biologically observed. It can extend to the ways that work within the computer and machine system. Nevertheless, the overall purpose of artificial intelligence to make human life easier remains the same. 

Earn yourself a promising career in data science by enrolling in the Data Science Online Training in Bangalore offered by 360DigiTMG.

Artificial Intelligence Course in Hyderabad

What Do We Understand by Machine Learning?

Machine learning can get defined as a subset or part of artificial intelligence. It is a process in which a machine gets “trained” or “educated” to learn and develop from its past skills and experiences. The subject of the teaching gets derived from the data collected and stored. Generally, it gets done when the said machine operated in the past.  

Machine learning involves a combination of the data with a set of required algorithms. It helps generate and deliver the final results. 

To summarize, machine learning is an application of artificial intelligence. It allows machines to understand and improve automatically from their experience.  Looking forward to becoming a Data Scientist? Check out the Data Science Course and get certified today.

What are the Points in Which Artificial Intelligence Differs from Machine Learning?

Although artificial intelligence and machine learning may seem the same, they are two entirely different concepts. The primary points of difference consist of the following:

  • Knowledge Acquisition

Artificial intelligence employs a system in which knowledge gets acquired. In addition to that, the concepts and ideas get applied and used for different and distinct purposes. 

On the other hand, machine learning merely refers to the system of acquiring and learning new ideas, skills, and concepts. The acquired knowledge conventionally does not get applied. 

  • Aim or Objective

Artificial intelligence aims to increase the possibility and chance of ultimate success. It does not target the accuracy aspect and element while doing so. Overall, it simulates and enacts cases and instances of natural intelligence. It gives it the ability to solve complex or complicated problems.  Also, check this Data Science Online Training in Hyderabad to start a career in Data Science.

The primary goal or objective of machine learning is the exact opposite. It works to increase accuracy. Conversely, it does not consider the success part. It allows machine learning to encompass a learning system from various data. It provides the machines with the capability of performing specific tasks. Furthermore, it aims to maximize their efficiency. 

  • Purpose or Application

AI entails a decision-making system where the machines mimic human cognitive skills to solve different and distinct problems. It seeks the optimal solution for any case. Moreover, it leads to wisdom and intelligence ultimately.  Become a Data Scientist with 360DigiTMG Data Science Colleges in Pune. Get trained by the alumni from IIT, IIM, and ISB.

On the other hand, machine learning entails a system that enables the machine to learn new information from pre-existing data. It involves the creation of self-learning and understanding algorithms. However, machine learning does not strive to seek any solution. Overall, it results in knowledge acquisition. 

Want to learn more about data science? Enroll in the Data Science Training in Chennai to do so.

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Data Analyst Courses In Other Locations

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360DigiTMG – Data Analytics, Data Science Course Training Hyderabad

2-56/2/19, 3rd floor, Vijaya towers, near Meridian school, Ayyappa Society Rd, Madhapur, Hyderabad, Telangana 500081
+919989994319

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How To Be More Effective in Financial Data Analysis

How To Be More Effective in Financial Data Analysis

Financial data analysis is one of the most important things within the accounting profession. There are different key areas that must be understood for a proper analysis to be effectively done. The analyst must understand the financial statement structure, the industry’s economic characteristics, and how the firm sets itself apart from the competition. 

Finance and accounting have been valuable assets to different businesses from the very beginning. With them, they bring great value through efficient operations or high revenue. The best technology that offers the most innovative value sources in this area is data analytics. Also, check this Data Analytics Certification to start a career in Data Analytics.

Data analytics is used in the finance and accounting field brings about a great boost in the profitability of any business and the reduction of operating costs. 

With analytics, many things can be achieved, such as identifying the spending habits of the consumers and market trends stock prices can be monitored using algorithmic reading. Unstructured data helps with improving the timeliness and broadening the scope. Today, accounting professionals use predictive and advanced analytics to leverage big data. Pursue a career in Data Analytics with the number one training institute 360DigiTMG. Enroll in the Data Analyst Course in Bangalore to start your journey.

Data Analytics Course in Hyderabad

Data analytics in finance and accounting

Data analytics has created great opportunities for finance professionals and accountants. They are now in a better position to offer better services in different areas, including:

  • Automation of tasks that are routine to reduce costs and improve accuracy
  • Making accurate predictions regarding the future industry and market trends
  • Getting a deeper and broader perspective on different business operations such as finances

Kickstart your career by enrolling in this Data Analyst Training in Hyderabad.

By understanding data analytics and its application in finance, professionals can become more effective in analyzing financial data. Firms often use advanced techniques to help capitalize on the large amounts of collected data. The main aim is growth and value done by leveraging different technologies. These include: 

  • Cloud storage and computing power: there has been great growth in this area. Datasets can be complex and very large. This is because some services offer scalable processing to meet the demand. 
  • Wide usage of data sources: internet service providers, government sources, mobile apps, and social platforms are widely used. Embedded devices are also available in most places. 
  • There is an existing digital infrastructure that is based on open-source software. Availability of open networks means that data specialists can have an easier time communicating with other experts in other fields like finance and accounting. 


Wish to pursue a career in data analytics? Enroll in this Data Analyst course fees in Pune to start your journey.

Adopting data technologies

Many financial and accounting service companies use technologies like data analytics even though the implementation rate is still quite uneven. 

Understanding the new technologies is bound to transform the industry. This is because the finance and accounting fields are slowly being reinvented.   

Changes are quite clear in business services and hiring practices. In turn, it has become possible for professionals in finance and accounts to expand services to improve the services offered. The approach of this industry on emerging technicities like artificial intelligence and analytics is also quite clear.     

Financial data analysis is becoming a big deal in all industries, and therefore, businesses need to embrace it. To be more effective in financial analytics, it is important to learn more about data analytics. The field needs to be prepared for analytics and other upcoming technologies. 

Don’t delay your career growth, kickstart your career by enrolling in this Best Data Analyst Course in Chennai.

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Data Analyst Courses In Other Locations

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360DigiTMG – Data Analytics, Data Science Course Training Hyderabad

2-56/2/19, 3rd floor, Vijaya towers, near Meridian school, Ayyappa Society Rd, Madhapur, Hyderabad, Telangana 500081

Contact Number: +91-9989994319

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Components Of Data Science Field

Important Areas And Components Of Data Science Field

When it comes to data scientists, half the tech professionals are eager to learn it. Why shouldn’t they be excited? After all, a data scientist is regarded as the sexiest job title of current times. Some say that data science is all about playing with data, turning them into understandable numbers and graphs. But, it is not that easy when one goes into detail. The data science field has various components, which makes it more complex. These components complete data science as a field of study. They need to work together to make a data science project a success. Here are areas of data science that every aspiring data scientist need to focus on:

 

Database management

Without data, there cannot be any data science. As the data and its structure are changing rapidly, the way to collect them, store them, and maintain them should keep updating as well. This is exactly what makes database management imperative to every business. Database management is useful in marinating data and ensuring that the data experts get the required data easily, without much hassle. Are you looking to become a Data Scientist? Go through 360DigiTMG’s PG Diploma in Data Science and Artificial Intelligence!

Data mining

Data mining is all about finding patterns hidden in the data. These patterns will help the business to make strategic decisions and understand the market better. Under this, the data is mined and given structures that will help in finding meaningful insights. These meaningful insights can be used to make decisions, curb risk and solve various problems. Also, check these Data Science Course fees in Chennai to start a career in Data Science

Machine learning

This is another component of data science, which is helping largely in predictive analytics. In machine learning, the algorithms are trained using past data. Now, these algorithms have become useful in predicting the outcomes of various scenarios. Thus one can say that machine learning has become a major part of data science and data analytics.

Data visualization

Simply finding insights from the data structures is never enough. Those who are not data scientists are simply unaware of how to understand these insights. Therefore, to make the data more understandable, data visualizations are used. This makes it easier to communicate the insights to the higher management.

Deep learning

Deep learning is another area that falls in the data science field. Deep learning is all about creating predictive models and is highly useful in predictive analytics. Neural networks are created, which work like the human brain. Data is fed into the neural network. Which then uses the same to predict the results of different situations. Become a Data Scientist with 360DigiTMG Data Science Training in Bangalore. Get trained by the alumni from IIT, IIM, and ISB.

Business intelligence

Data science is capable of helping management to make decisions. Using business intelligence tools, the management can track trends and recognize patterns in the business’s data. This can help in making better decisions with more informed knowledge of the industry and market.

Want to learn more about data science? Enroll in the data science institutes in Hyderabad to do so.

Cloud computing

Lastly, cloud computing stands to be one of the most required parts of data science these days. As the amount of data collected and used by businesses increases, the infrastructure required to handle such amounts is becoming ineffective. This is where cloud computing comes in. Using cloud applications; businesses can store and manage data easily without investing in heavy infrastructure.

As data science is prevalent in all industries, the number of job opportunities is growing rapidly. Even today, there is a significant gap between the demand and supply of manpower in data science. This is the right time to be part of the Data Science Training to get complete theoretical and practical knowledge and industrial exposure for a brighter future. 

Earn yourself a promising career in data science by enrolling in the Data Science Course fees in Pune offered by 360DigiTMG.

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360DigiTMG – Data Science, Data Scientist Course Training in Bangalore

No 23, 2nd Floor, 9th Main Rd, 22nd Cross Rd, 7th Sector, HSR Layout, Bengaluru, Karnataka 560102

1800 212 654 321

Email: enquiry@360digitmg.com

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