Master Data Science Course with Machine Learning, Deep Learning & AI

Acadgild’s Data Science Masters will make you a skilled data scientist in just six months. It covers all the essentials of the field and provides plenty of hands-on experience.

100+ hrs
Live Mentoring
250+ hrs
Coding Assignments
Real Time Projects
Industry Cases
Coding Support


16 Weeks
Total Duration
Course Price



Fundamentals of Python and R

  • Basics of Python and R
  • Conditional and loops
  • String and list objects.
  • Functions & OOPs concepts.
  • Exception handling.
  • Database programming.

Data scientists must know how to code - start by learning the fundamentals of two popular programming languages Python and R.

Data Wrangling

  • Reading CSV, JSON, XML and HTML files using Python
  • NumPy & pandas
  • Relational databases and data manipulation with SQL
  • Scipy libraries
  • Loading, cleaning, transforming, merging, and reshaping data

Once you have the core skill of programming covered – dip your feet in the nitty-gritties of working with data by learning how to wrangle and visualize them.

Statistics and Probability

  • Probability mass functions
  • Probability distribution functions
  • Cumulative distribution functions
  • Modeling distributions
  • Inferential statistics
  • Estimation
  • Hypothesis testing
  • Implementation of statistical concepts in Python

It is impossible to use data without knowledge of statistics. Collect, organize, analyze, interpret, and present data using these concepts of statistics.

Artificial Intelligence

Learn how AI can help solve real-world problems using machine learning & deep learning.

The content includes applied aspects of artificial intelligence: 20+ practical assessments to strengthen learning alongside clear, targeted and actionable feedback. 5+ end-to-end case studies supported real-world business problems across varied industries that offer students a style of real-time expertise.

Machine Learning Models in Python

  • Building models using below algorithms
  • Linear and logistics regression
  • Decision trees
  • Random forests
  • XGBoost
  • K nearest neighbour & hierarchical clustering
  • Principal component analysis
  • Text analytics and time series forecasting

Machines have increased the ability to interpret large volumes of complex data. Combine aspects of computer science with statistics to formulate algorithms that help machines draw insights from structured and unstructured data.

Data Visualization Using Matplotlib and Tableau

  • Interactive visualizations with Matplotlib,
  • Data visualizations using Tableau
  • Tableau dashboard and story board
  • Tableau and R integration

Complex data sets call for simple representations that are easy to follow. Visualize and communicate key insights derived from data effectively by using tools like Matplotlib and Tableau.

Deep Learning Using TensorFlow

  • Basics of neural network
  • Linear algebra
  • Implementation of neural network in Vanilla
  • Basics of TensorFlow
  • Convolutional neural networks (CNNs)
  • Recurrent neural networks (RNNs)
  • Generative models
  • Semi-supervised learning using GAN
  • Seq-to-seq model
  • Encoder and decoder

Go beyond superficial analysis of data by learning how to interpret them deeply. Use deep-learning nets to uncover hidden structures in even unlabeled and unstructured data using TensorFlow.

Handling Big Data with Spark

  • Introduction to Big Data & Spark
  • RDD's in Spark, data frames & Spark SQL
  • Spark streaming, MLib & GraphX

Lastly, manage your infrastructure with a data engineering platform like Spark so that your efforts can be focused on solving data problems rather than problems of machines.

Capstone Project

The course culminates in an enterprise-level project for a fictitious client that will expose you to every stage of the data science process – from data acquisition and preparation to evaluation, interpretation, deployment, operations, and optimization. Every student is guided by industry experts as they bring their personal projects to life. Alternately, students may choose to work on a live project from their organization. Our students generally come from varying backgrounds. We encourage all our students to pursue projects that are best suited for their careers and domains. The project is an opportunity for you test your skills and demonstrate your ability to invent solutions for real world problems.

Speak to our course advisor if you have queries

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The Acadgild Experience

Live Sessions

Expert Mentors conduct live sessions throughout the course
Master technology through intensive online sessions

24x7 Support

24x7 Coding Support
Our support team will be there for you around-the-clock to help you with doubts.

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100% Job Placement Assistance
Our team will guide you to your dream job

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How is the experience of Data Science Course at Acadgild?

The data science course comes with a free job assistance program where coaches will help you get industry ready. With our data science course, students can build portfolios which will help them land a job they prefer. The data science course contains relevant details and knowledge packaged in a holistic manner so that students can perform best in their careers. Whether you are a seasoned professional, fresher or someone seeking to skill themselves in new technologies, this course can help you learn more and do more with your career. The data science course is designed to help students get the best out of their course. For this, Acadgild has a coding support team to aid students in case they encounter any roadblock.
Whether you are a seasoned professional, fresher or someone seeking to skill themselves in new technologies, our data science course can help you become a data scientist, learn a new technology and do more with your career. Enroll Now for the data science course at Acadgild

What is this course about?

The course teaches statistics for business analysis, machine learning algorithms, deep learning with TensorFlow, and programming with Python. It will help you explore, analyze, and interpret different kinds of data.

Who should take this course?

The course is suited for anyone with a zeal to learn about data science. It is ideal for aspiring data scientists from different backgrounds. Our students are generally analysts, developers, managers, information architects, researchers, and other working professionals looking to advance in the field of data science.

What are the prerequisites for this course?

Prior knowledge of Python and statistics is useful. Nonetheless, these are covered in the course as well.

What are the software and hardware requirements?

• Microsoft® Windows® 7/8/10 (32- or 64-bit).
• 4GB RAM minimum, 8 GB RAM (recommended).
• i3 or higher processor.
• Internet speed: Minimum 1 Mb/s.
• Intel® VT-x (Virtualization Technology) enabled.

Will I develop my own project while taking this course?

Yes, you may. Our curriculum is comprehensive and will make you capable to create your own project. Our support staff is also committed to help you along the way. Alternately, you can work on one of the projects in our repertoire to implement what you learn.

How many hours would this course require every week?

The course will take 10-15 hours of your time every week.

Can I learn data science without a technical background?

The short answer is – yes, you can! Knowledge of math, quants and programming is handy, but the desire to solve problems and an eagerness to learn statistics can help you overcome the lack of a technical background.

Where can I learn Python programming?

You can learn Python programming on Anaconda, which hosts popular Python libraries like SciKit Learn, NumPys, Pandas and others. It also consists of environments like the Jupyter Notebook and programs like IPython that reduces the time it takes to import libraries.

Is it mandatory to learn coding and statistics for data science?

Yes, coding and statistics are important skills in data science. Knowledge of linear algebra, calculus, probability, math, statistics, and basic programming in Python or R is useful while learning data science.

What are the key skills needed to implement AI?

Some of the key skills needed to implement AI are Machine Learning, Deep Learning, Natural Language Processing (NLP), Statistics, Probability, and Linear Algebra.

What is the difference between Big Data Analytics & Big Data Engineering?

Big Data Engineering includes all processes that aim to increase the accessibility of data from various sources and optimize it for analysis. Big Data Engineers use tools like Hadoop, MapReduce, NoSQL and MySQL for their tasks.
Big Data Analytics, on the other hand, focuses primarily on the process of data analysis. It involves creating reports that include graphs and infographics to effectively communicate insights from data. Data Analysts use programs like SQL, Hive, Pig, Matlab, R, Excel, etc.

Do all students in a batch generally have the same knowledge of data science?

Our students are generally professionals from different fields with varied levels of experience. The course is designed to help anyone interested in data science learn it so long as they have basic knowledge of programming and math and decent reasoning ability.

How are the Data Science and Data Analytics courses different?

The data analytics course is for professionals interested in learning data analysis and visualization. It covers R, Tableau and Excel. The data science course, on the other hand, focuses on processes like data cleansing, processing, and predictive modelling. It is for professionals interested in topics like machine learning, deep learning, and Python programming.

What separates data scientists from big data developers?

Data scientists work with data according to business needs. They are responsible for data analysis. Big data developers design and implement programs that make the analysis possible.

Tell me more about the Job Placement Assistance Program.

Our job placement program offers students one-on-one career counselling, and the chance to work with our corporate partners.
Candidates who fulfill the following criteria will be eligible for the program:
• Scored 75% marks or above (resulting in a Platinum certificate) in the course.
• Successfully completed at least 2 quality projects.
• Scored 80% in all the mock technical interviews.
• Was never found plagiarizing code.

*This feature is currently available only for students in India.

How much do data scientists earn?

Data scientists earn Rs 6,50,000 on average according to Glassdoor. Experienced data scientists make as much as Rs 20,00,000.

Why should I invest in a data science career?

Data science offers plenty of lucrative opportunities to professionals with the right skills. There is not only a growing demand for data scientists, but also a shortage of talents in the market. These are good reasons for you to invest in a data science.

What are some of the companies hiring data scientists?

Data scientists work across industries. Some of the companies hiring data scientists include Google, Yahoo, LinkedIn, Facebook, Amazon, IBM, Wipro, HCL, etc.

What if I miss a class?

All sessions are recorded and uploaded to the course dashboard for you to access at your convenience.

Can I choose my mentors?

Our mentors are top-notch industry professionals with at least 5 years of experience. You will be taught by the best in all batches.

What is Acadgild?

Acadgild is one of India’s premier online platforms for technical training. Our courses help freshers and experienced professionals learn the most in-demand skills in the market from industry experts, who blend traditional methods of teaching with new, digital ones for an unmatched online learning experience.

How do I request for more information?

We believe in continuous learning and grant our students lifelong access to the course dashboard. The platform is gamified to make learning fun and interactive. Our students engage in quizzes, assignments and projects to gain practical work experience and to build a solid portfolio. They receive 24*7 support along the way. Our certificates are internationally recognized, and we even offer job assistance to our gold and platinum students.

How do I make the payment for this course?

You can pay after registering for the course. We accept most credit and debit cards. You can also pay via net banking. Our payment portal has an EMI option if you wish to pay in installments.

What is the Refer and Earn program?

Our ‘Refer and Earn' program gives you a discount on the course fees when your references join us. You may refer students by writing to us at
The details of the Refer and Earn policy can be found at

What is the Refund Policy?

To request a refund, write to us at You may apply for a refund in the first three days after paying the fees. No requests will be entertained after this initial period. The terms and conditions of AcadGild's refund policy may be revised without prior notice. Please check websites for updates on this policy.

How do I request for more information?

You can write to us at with your contact details. Our representatives generally respond to requests within 24 hours.