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Data Science Course - Become a Certified Data Scientist

Advance your Career in Data Science & Machine Learning with Entri Elevate. Equip learners with in-demand skills, Real projects, Trending Tools & Technologies. Enroll now!

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An Overview of Data science and Machine Learning Course

Entri Elevate offers a Professional Certificate Program in Data Science and Machine Learning, which is a highly advanced certification course designed for learners. This comprehensive course is designed by industry experts and experienced trainers and provides hands-on exercises and projects that allow students to apply what they have learned to real-world scenarios. Upon completion, students will have gained a solid understanding of the skills and talents needed to build and deploy machine-learning models for various applications.

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Inclusive & Immersive Hybrid Training Sessions

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Industry Expert Sessions

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80+ Live & Recorded Sessions

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Soft Skill Sessions

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Industry Networking

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Placement Training

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Illinois Tech Certification

Skills Covered

Here are the top skills that will make you stand out in the field of data science and machine learning

Placement Stories

Get a peek into the remarkable accomplishments of our aspirants.

Why Do Students Love Us?

Hear from Our Data Science Students!

Tools Covered

Illuminate the path to insights! Unlock the power of data science and machine learning with these necessary tools!

Data Science Job Roles

What Exactly Does a Data Scientist Do? Lets Dive Deep into Roles!

Data Scientist

A data scientist collects, analyzes, and interprets large sets of data to identify patterns and trends. They use various tools and techniques to extract insights and make data-driven decisions. With a data science and machine learning course, individuals can acquire skills in data mining, programming languages, and statistical analysis, making them suitable for this role.

Machine Learning Engineer

Machine learning engineers work on developing algorithms and models that enable machines to learn from data and make accurate predictions. These professionals have a strong background in mathematics, programming, and data science. A course in data science and machine learning provides individuals with a solid foundation in these areas, making them well-equipped for this role.

Data Analyst

Data analysts gather, organize, and interpret data to provide valuable insights to businesses. They use statistical tools and techniques to identify trends and patterns and communicate their findings to key stakeholders. A data science and machine learning course equips individuals with skills in data manipulation, visualization, and analytics, making them a perfect fit for this role.

Business Intelligence Analyst

Business intelligence analysts are responsible for studying data to uncover trends and patterns that can assist companies in making informed decisions. They employ a variety of data analytics tools to collect information from various data sources and present it in a format that can be easily comprehended by stakeholders. This position necessitates strong analytical abilities, business expertise, and excellent communication skills.


The curriculum is designed to help you learn the skills required to become a successful professional.

  1. Introduction to Programming
  • What is programming?
  • Compiler,Interpreter
  • Source Code
  • Machine code
  • Algorithms
  • Editors
  1. Introduction to Data Science
  • What is Data Science?
  • Job Roles
  • Terminologies
  • Data Science Applications and its work flows

  1. Language Introduction and Installation
  • Python history
  • Python features
  • python and pycharm installation.
  1. Python Basics
  • Print command
  • Comments,escape sequences
  • Variables
  • Data types
  • User interactive command, operators
  1. Conditional and looping statements
  • Selection statements
  • Control statements
  • Break and continue statements
  • Nested loops
  1. Data Structures in python

Introduction to user defined data structures and non-primitive data structures:list,

dictionaries, set,tuples, strings and sequences,accessing and modifying elements in data

structures,comprehension: list, set and dictionary.

  1. Functions in Python

Defining functions, passing arguments to functions, different types of arguments, returning

values from functions, local and global namespace, lambda function, recursion, filter,

map,reduce, eval. Generators and decorators.

  1. File Handling and Exception Handling

File processing, Reading and writing files using ‘with’ statements. What is an Exception?,

raising and catching exceptions and handling errors gracefully using try-catch-finally.

  1. Object Oriented Programming

Introduction to OOPs, Classes and objects, inheritance and polymorphism, encapsulation

and abstraction.

  1. Modules in Python

Introduction to modules, importing modules, creating and using modules.

  1. Regular Expressions

Defining regular expressions, using regular expressions with python.

10.Pandas and Numpy

Introduction to Pandas library, reading and writing data with pandas, data cleaning and

exploration with pandas. Introduction to numpy, numpy basic operation.

  1. Data Visualization with Matplotlib and seaborn

Introduction, basic and advanced plotting technique

  1. Introduction to SQL
  • Introduction to databases and database management system
  • Overview of MySQL
  • Installing and getting started with MySQL workbench.
  1. SQL Database
  • Creating databases
  • Dropping databases
  • Introduction to tables
  • Data types in MySQL
  1. Data Definition Language
  • Introduction DDL commands
  • Creating table
  • Modifying table using ALTER, DROP, TRUNCATE, RENAME
  1. Data Manipulation Language
  • Overview of MySQL SELECT statement
  • Retrieving data
  • Filtering and sorting
  • Joining and combining data
  • Updating and deleting data
  • Inserting data
  1. MySQL Functions and Joins

MySql built-in functions, inner, outer and self joins and combining data from multiple tables.

  1. MySQL subqueries and views

Subqueries and nested queries,creating and using views, normalization and denormalization

  1. MySQL stored procedures and triggers

Creating and using stored procedures and triggers, database security and user management.


Build a simple database to help us manage the booking process of a sports complex. The sports complex has the following facilities: 

  • 2 tennis courts
  • 2 badminton courts,
  • 2 multi-purpose fields
  • 1 archery range.

Each facility can be booked for a duration of one hour. Only registered users are allowed to make a booking. After booking, the complex allows users to cancel their bookings latest by the day prior to the booked date. Cancellation is free. However, if this is the third (or more) consecutive cancellations, the complex imposes a $10 fine

  1. PowerBI Introduction
  • Introduction
  • PowerBI
  • Power query editor
  • PowerBI service, 
  • Installation and configuration of powerBI desktop
  • Connecting to data sources and loading data
  1. Data modeling in PowerBI

Understanding data modeling and relationships creating relationships between tables, building hierarchies and calculated columns, transforming using power query.

  1. Data Visualization in PowerBI

Understanding data visualization best practices, building basic visualizations(charts, graphs, tables etc..), formatting and customizing visualizations.

  1. Building Interactive reports 

Using filters and slicers to create interactive reports, building drill-through reports and drill-down visualizations, creating bookmarks and scenarios.

  1. Creating Dashboards

Building dashboards with multiple visualizations, creating dashboards tiles and adding images, creating custom visuals using powerBI

  1. Data Analytics with PowerBI 

Understanding data analytics and DAX language, creating DAX formulas and measures.

  1. Project

Financial Performance analysis: to optimize financial reporting in a firm that provides customers to track their financial health and productivity. Create data models and visualizations, and dashboards and present the project results and insights.

  1. Probability & Statistics

Definition of probability,conditional probability,independent events, Bayes' rule, Random variables, discrete random variable, continuous random variable, probability density function, mean, median, mode, Standard deviation, correlation, correlation coefficient. Testing of hypothesis, confidence interval, Chi-squared test, t-test

  1. Introduction to Machine Learning

Machine learning concepts and applications,Overview of Supervised, Unsupervised, and Reinforcement learning.Exploring common machine learning algorithms

  1. Data Preprocessing for Machine Learning

Types of Data: Categorical and Numerical Data, Understanding data preprocessing and its importance in machine learning, Handling missing data, outliers, and categorical variables, Label Encoding, Feature scaling and normalization techniques.Python Library: Numpy, Pandas, Sklearn

  1. Project

Use a dataset then remove NaN values and apply label encoder and scaling methods.

  1. Supervised Learning : Regression

Understanding regression analysis and its use in machine learning, Building linear regression models,

Evaluating model performance and making predictions.

  1. Project using Regression

Create a model using Linear regression algorithms and predict using the best algorithm among them.

  1. Supervised Learning

Classification Understanding classification analysis and its use in machine learning, Building logistic regression, Support Vector Machines, Random Forest, K-Nearest Neighbor, Naïve Bayes and Decision tree models. Evaluating model performance using different classification accuracy metrics and making predictions.

  1. Project

Create a model using different algorithms for a given dataset and choose the best algorithm among them.

  1. Unsupervised Learning

Clustering Understanding clustering analysis and its use in machine learning, Building k-means and hierarchical clustering models,Evaluating model performance and making predictions,

  1. Project

Create a model using different algorithms and predict using the best algorithm among them.

  1. Unsupervised Learning

Dimensionality Reduction Understanding dimensionality reduction and its use in machine learning, Building principal component analysis (PCA) and t-SNE models, Evaluating model performance and making predictions.

  1. Natural Language Processing

Basic concept of NLP, Data Cleaning: remove punctuations, tokenization, remove stop words, stemming, lemmatization.Packages of NLP : NLTK (Natural Language ToolKit), Pattern, TextBlob,Vectorization techniques: Bag of Words, TF-IDF

  1. Project

For the given paragraph do the following: word_tokenise and sent_tokenise, using stop words eliminate most common words and do stemming and lemmatization.

Deep learning basics: Neural Network, perceptron,Back-Propagation, Activation functions, Deep networks, Regularization, Dropout, Batch Normalization.Python libraries for Deep learning : Keras, Tensor flow. Convolutional neural networks: Introduction to CNNs, Convolution, Correlation, FIltering. CNN architectures, Compiling and fitting a model Advanced Deep architectures:Recurrent Neural networks (RNNs), Advanced RNN: LSTM


Create a deep network model using CNN and calculate its accuracy and loss values.

Data Science Blogs

Explore Trending Topics of Data Science and Machine Learning

Top 12 Data Science Final Year Project Ideas 2024

A data analytics project may be one of the requirements for receiving your degree. It could be challenging to select the top Data Science Projects for your senior year. If you don’t have a lot of free time, many of them have a steep learning curve and may not be the ideal choice.

Best Books for Data Science Beginners

Here are the list of the best books for data science beginners that will help you learn the subject better. We are also providing a brief description regarding all the picks so that you can pick depending on your convenience.

Exploratory Data Analysis Techniques

EDA makes sure that the proper patterns and trends are made available so that the model may be trained to produce the desired results, much like a good recipe. As a result, using the appropriate EDA tool and appropriate data will help accomplish the desired result.

Data Science Learning Path in 2024

Data Science plays a crucial role in addressing some of the world’s most pressing challenges, such as healthcare, climate change, and social inequality. As the demand for data scientists has increased, Let's read about the learning path to become a data scientist in 2024.

Eligibility /Pre-requisities

Anyone with an interest in data science and machine learning can enroll in these courses. There is no specific educational background required. However, a basic understanding of mathematics, statistics, and programming can be beneficial.

Basic Programming Skills icon

Basic Programming Skills

Analyzing and interpreting complex datasets, developing machine learning models, and providing actionable insights to solve business problems.

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Logical thinking

Having the ability to think critically and approach problems analytically by identifying its key components, and understanding how they relate to each other.

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Problem-Solving Skills

Formulate data-driven solutions, identify patterns and trends, and ability to apply logical thinking to solve complex problems with strong problem-solving and analytical skills.

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Basic Mathematical skills

Mastering fundamental mathematical concepts, like algebra, calculus, and probability, is essential. These concepts serve as building blocks in data science and are applicable in various fields.

Our Hiring Partners

Connect employers with a global network of recruitment partners to meet their hiring goals.

Why Live Sessions?

Live sessions on data science and machine learning have several advantages that make them a valuable learning resource.

Live Sessions

Real-time interaction

Live sessions allow learners to interact with instructors and peers in real time, promoting community and creating an environment for asking questions, receiving feedback, and learning from others.

Flexible and Convenient

Learners can participate from anywhere with an internet connection, making it easier to fit learning into their busy schedules. Also, if learners miss a session, recordings are often available for later viewing.

Up-to-date information

Live sessions are up-to-date with the latest trends and technologies. Instructors can share their expertise on emerging topics and provide insights into industry trends. This information helps learners stay current and competitive in the field of data science and machine learning.


Compared to traditional classroom-based training, live sessions are often more affordable, making them accessible to a broader audience. This affordability also makes it easier for organizations to provide ongoing training and development opportunities to their employees.

Your Mentor

Gain an edge in your career by accessing our team of experts who will provide invaluable guidance and support. Meet our team of mentors!

Data Science Intructors

Learn From Industry Experts

Data Science With Python Exam & Certification

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illinois Tech

Frequently Asked Questions

Illinois certification validates your proficiency and expertise across various industries. It can enhance your credibility and positively impact your career growth, earning potential, and professional fulfillment. Obtaining Illinois certification opens up opportunities for career advancement and high-paying jobs, bringing ample career opportunities and enhancing your professional success.

Illinois certification is best for IT freshers. This certification program provides comprehensive training and education on various aspects of IT, including programming languages, software development, database management, and more.

Illinois certification helps freshers build a strong foundation in IT. It equips them with the necessary skills to succeed in their careers and to get better job opportunities with higher salaries.

What Our Students Are Saying

Enrolled by Students
4.8 (89 Ratings)

Key Learning Outcomes of Data Science and Machine Learning Course

The key takeaways from the "Entri Elevate Data Science of Machine Learning" course are as follows

Master the Latest Analytics Tools

Enhance your knowledge of the most popular analytics tools and technologies.

Empower Yourself with Data Analytics Skills

Develop the skills to use analytics and data science independently to solve business problems.

Mastering Business Insights

Learn to extract crucial business insights from data and present them clearly to stakeholders.

Predictive Modeling for Business Solutions

Build models that can predict future trends and leverage them to solve business problems.

Learn About Practical ML Applications

Apply state-of-the-art ML algorithms to create solutions for real-world business problems.

Learn About AI Implementation

Develop an AI strategy for your industry and evaluate the various factors involved in its implementation.

Live Sessions

Frequently Asked Question about Data Science Course

Data Science is the process of analyzing and interpreting large amounts of data to extract useful insights and knowledge

Data Science is a broader field that involves using various techniques, algorithms, and tools to extract insights and knowledge from structured and unstructured data. Data Science involves not only analyzing data but also developing predictive models, creating machine learning algorithms, and building data-driven products. On the other hand, Data Analytics focuses on extracting insights and useful information from data by analyzing it. It is a narrower field that involves collecting, processing, and performing statistical analyses on data. The main goal of Data Analytics is to uncover patterns and trends in data that can inform business decisions or improve operational efficiency. In summary, Data Science is a more comprehensive field that includes Data Analytics, along with other techniques and tools for data processing, modeling, and product development.

Data analytics is the process of examining raw data using statistical and quantitative techniques to extract meaningful insights and information. It involves collecting, cleaning, processing, and analyzing data to identify patterns, relationships, and trends that can be used to make informed decisions. Data analytics is often used in scientific research, healthcare, and other fields where there is a large amount of data to be analyzed. Business analytics, is the use of data and statistical methods to drive business decisions and improve performance. It involves using data to identify opportunities for growth, optimize business processes, and make better decisions. It is often used in marketing, finance, and operations to improve performance and increase profitability. In summary, data analytics focuses on the analysis of data to uncover insights and information, while business analytics focuses on using data to drive business decisions and improve performance.

We would like our learners to follow our curriculum and timeline to get the best out of this program. This program is time bound and our mentors will be following the curriculum to the doubt

We have 4 kinds of sessions in this program Core session: The curriculum will be covered in this session. Coding skills session : Basic coding skills and the latest coding trends will be covered in this session Soft skill session: Communication skills, Interview skills, GD skills, English communication etc will be covered in this session Industry Expert session: Experts from global companies will give an insight about the latest industry relevant topics, career journey, best practices etc. Core sessions: 1 - 2 sessions per week will be there with a timespan of 1 - 1.5 hours. Maximum 30 learners per batch.

Coding Skill, Soft Skill and industry expert sessions will be 1 session per week. This will be multiple batches combined sessions to drive community engagement.

Operating Systems: Windows 10 or 11 (Home or Pro), Mac: High Sierra or later, Linux: any recent distribution CPU; Intel i5 minimum,Intel i7 or i9 preferred. Hard Drive: Minimum 256GB RAM: minimum 4GB

Learners will be given a capstone project which will be an industry relevant project. The Capstone project needs to be done as a team. Once the capstone project is completed within the stipulated time frame, our academics team will evaluate the outcome and once the completion is approved, you will be awarded the internship certificate.

The recorded videos in the app will be available for 1 year from the purchase date. Each batch (live sessions) will have a duration of 8 - 9 months.

Having a background in Computer Science, Mathematics, and Statistics can be very helpful in studying data science. However, it's not a strict requirement. Data science is an interdisciplinary field that involves many different areas of expertise, including computer programming, statistics, mathematics, and domain-specific knowledge. For example, if you are interested in data science in the healthcare industry, having a background in healthcare would be beneficial. Similarly, if you are interested in data science in the finance industry, having knowledge of finance and economics would be helpful. That being said, a solid understanding of computer programming, mathematics, and statistics is crucial in data science. These skills are necessary to collect, clean, analyze, and visualize data. Without these skills, it can be difficult to manipulate data effectively and draw meaningful insights from it. Therefore, while having a background in computer science, mathematics, and statistics is not an absolute requirement to study data science, it is highly recommended to have a strong foundation in these areas.

For a data analyst / business analyst job, learning coding is not mandatory, but it is beneficial. Having some knowledge of programming will enhance your ability to work with data and increase your efficiency and improve the job prospects. For a data scientist / Machine learning engineer job, coding is an absolute must.

Data Analyst, Data Scientist, Financial Analyst, Market Researcher, Operations Research Analyst, Business Analyst, Business Intelligence Executive, Strategy Consultant etc.

Career support team will get in touch with the learner towards the end of the program. They will help the learner in resume building, interview skills and LinkedIn profile building. Upon successful completion of the program, the career support team will match the job opening with the eligible candidate and will refer the candidate to the hiring partner. It is to be noted that the selection is completely hiring partner’s prerogative and it depends on the company hiring policies.

For data analyst you can expect the salary to start from 2LPA and can go up to 10 LPA. This completely depends on the company, the job location and primarily your skill set

Your chances of getting a job depends on multiple factors. Your skill set and the experience you gain by doing a lot of real world projects will help. However companies have hiring policies wherein they have restrictions on hiring people with extended career break, some academic backgrounds, age etc. But if you complete this program and gain enough experience and skills, you will be able to get freelance opportunities and earn even if you do not meet hiring eligibility criteria. Also, the career support team is region agnostic, which means we cannot guarantee an interview based on your location preference. Interviews will be arranged based on the matching skill set and eligibility with respect to the job opening. We have hiring partners Pan India.

Software Engineers who wish to be Data scientists / Data Analyst / Business Analyst etc Entry level graduates who wish to start their career as data analysts Marketing / Finance / Healthcare professionals who wish to apply analytics Non technical business analysts Learners from any background can join the course who are interested in solving problem through data

The program can be taken by anyone who is interested in problem solving with a data centric approach. However the program is best suited for learners who have an undergraduate degree. High School and associate degree / diploma graduates can also pursue this program

After completing payment, users will get a welcome call from the User Happiness team within 24 hrs and a form will be shared to complete the profile. Users will be added to platforms (Slack / WhatsApp group) before the commencement of the batch.

You can watch the recorded videos on your mobile. However, to do the tasks and projects given to you, you should have a functioning laptop.

You can expect to learn problem solving with a data centric approach. How data can be used to solve business problems and take important business decisions. The curriculum consists of recorded content, live sessions, industry expert sessions, career coaching sessions and projects which will make you a skilled data science professional.

Just by joining this program, a new job / job switch should not be expected. You should be committed and devote 12 - 15 hours per week on learning, attending live sessions, and completing the projects given. You won’t be able to progress unless you are committed and focussed.

It would require at least 12-15 hours of time commitment per week from the learner’s side.

Eligibility for Refunds: Learners can avail a refund within 30 days of joining the batch. No refund request will be entertained under any circumstance after 30 days of joining the batch. Refunds are not applicable to the recorded video subscriptions. Refund Process: To request a refund, learners must contact our User Happiness Team and raise the refund request. Our team will analyze the case and decide on the refund. If approved, learners will receive the refund within 30 working days after approval. Learners will continue to pay the monthly EMI for loan (if applicable) and such loan cannot be canceled. Refund Amount: Refund amounts will be determined based on the following conditions (Once your refund request is approved): Before the batch starts, there will be an operational cost deduction of 20% of the paid amount or Rs 3000 whichever is the highest Once the batch starts, a deduction of 40% of the paid amount or Rs 6000 whichever is highest will be applicable. No refund requests will be entertained after 30 days from the date of batch commencement. Exclusions: Refunds are also not applicable to learners who have completed more than 30 days in the batch. Changes to the Refund Policy: We reserve the right to modify or update this refund policy at any time without prior notice. Any changes to the policy will be posted on our website Scenario Before batch commencement : 20% of the program fee paid or Rs 3000 (Whichever is highest) Post batch commencement up to 30 day: 40% of the program fee paid or Rs 6000 (Whichever is highest) Post 30 days of batch commencement: No Refund

If a Learner, due to unavoidable circumstances is unable to commence with the batch and requests for a deferral before the batch Commencement date, Learner will have an option to defer to another batch. There is no cost involved in changing the batch. However, you can raise a maximum of 1 batch change request and our academics team has to approve the batch change request. A Learner can request for deferral once to a batch which starts within the next 6 months from the batch start date of the initial batch the Learner enrolled for. For example if the initial batch commencement was January 1, the deferral batch commencement date should be within June 1.

Learners can apply to defer a batch till 30 days from the commencement of the batch. Only one deferral request will be entertained once the batch commences. No additional fee is applicable for deferrals Post 30 days, no batch deferrals will be entertained The deferred batch will be a fresh batch and not from where the user paused the initial batch. Deferral policy Scenario: Before batch commencement :No deferral fee. Learners can do one batch deferral request provided the batch is not commenced. The deferred batch commencement should be within 6 months from the first batch commencement date learner opted for. After batch commencement : Learner can request once for batch change / deferral within 30 days of batch commencement No deferral fee

Take these important steps to become a skilled data scientist. Start by laying a strong academic foundation by earning a bachelor's degree in a subject like statistics or computer science. This develops fundamental programming, arithmetic, and statistical abilities. Next, learn essential programming languages for data analysis, such as R or Python. Develop your skills with tools like Pandas, NumPy, and SQL to manipulate data effectively. Utilizing tools like scikit-learn, put your understanding of machine learning principles and algorithms into practice. Display your abilities with a wide range of projects that tackle practical issues in the real world. In this constantly evolving sector, it is essential to never stop learning. Consider pursuing higher degrees or certifications, as well as continuing education to keep current on trends. Make connections with the data science community through online discussion boards and conferences to learn from experts' experiences and views. To improve your marketability and obtain real-world experience, look for internships or entry-level jobs. A solid educational background, a varied portfolio, ongoing education, networking, and real-world experience are all necessary for success in the fast-paced area of data science.

Indeed, machine learning and data science will make great careers in 2024. There is a strong market for qualified machine learning engineers and data scientists, and the pay is attractive. Furthermore, because the sector is perpetually changing, there are always fresh chances for development and learning.

Advantages of Learning Elevate Data Science of Machine Learning Course

Elevate Learning Experience

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Student Support

Available all-day, using Slack for urgent queries.

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Q&A Forum

Timely doubt resolution from peers and mentors

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Expert Feedback

Personalized feedback on assignments and projects

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Industry Networking

Live doubt clearing sessions with Industry Experts

Career Support

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Personalized Industry Mentorship

Get personalized feedback and mentorship from an experienced data science specialist to enhance your career path.

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Resume Review

Get personalized guidance on your resume structure and content.

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Live profile-building workshops

Build your profile with hands-on sessions, whether on your résumé, GitHub, or Kaggle platform.

Upcoming and Recorded Webinars

Who Can Apply For the Course

  • Students
  • Professionals
  • Career Switchers
  • Job Seekers
  • House Wife
  • Un-employers
  • Freelancers
Who can apply
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