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Data Science Course with Machine Learning, Deep Learning & AI

4.7 (500+ Ratings)

Acadgild’s Data Science Course 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.

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60+ Hrs

Live Mentoring

200+ Hrs

Coding Assignments


Real World Projects

24 x 7 

Coding Support

Syllabus For Online Data Science Course

Essentials of Python & 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.

* Sessions on R are not live, but self-paced.

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.

* Sessions on Tableau are not live, but self-paced

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 and 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.

* Sessions on Handling Big Data with Spark are not live, but self-paced.

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.

Job Assistance

After completing your assignments and projects, we will provide you with one-on-one career guidance, conduct mock interviews and help you build a professional online portfolio to help you get noticed by top recruiters.

Resume Building

Learn what to put in your resume and how to get noticed on top job portals from industry experts.

Online Reputation Building

Establish your presence on all the right social networks like Git, Stack Overflow, LinkedIn, etc

Mock interviews

Get insider tips on how to ace interviews at top firms.

Landing A Job

Work at top MNCs and start-ups through by putting your best foot forward in our placement drives.

Capstone Domain Projects


Image Analytics


Text analytics

Data from commercial research has always had a say on how manual tasks are conducted in the pharmaceutical industry. With advancements in machine learning and artificial intelligence, data now plays an even more significant role in prescribing the best medicines for very specific medical conditions. Prescribing the right medicine is essentially a classification problem. At Acadgild, we focus on Image Analytics, NLP, Text Analysis and Predictive Analytics using CART (Classification and Regression Tree) models.


Churn Predictions

Sentiment Analysis

Customer Segregation

Time Series Forecasting

Data is arguably most influential in the e-commerce industry. Needless to say that machine learning and artificial intelligence play a crucial role in the development of this domain. These technologies help gather, analyse, and make sense of data regarding customer habits in real-time to make suitable predictions. For instance, the moment someone adds a product to their cart, websites and e-commerce platforms can recommend other products that the same customer is most likely to buy thereafter. Machine learning and artificial intelligence is also useful in anomaly detection, customer care and pricing. Acadgild issues several case studies that involve churn predictions, sentiment analysis, customer segregation and time series forecasting – all of which are crucial in e-commerce.


Feature Engineering Techniques

Machine Learning

Data science relies on information from a variety of sources to make effective predictions. In a well-documented domain like finance, it has created plenty of opportunities to understand customers and deliver better products and experiences. Tasks like generating a credit-score for potential customers has become a lot easier. Acadgild uses case studies from finance to help learners understand the practical implementations of feature engineering techniques and other machine learning concepts to solve real-world problems.



Deep Learning

Machine learning is transforming the telecommunications industry. It is having a significant impact in network automation, and more importantly, on customer experience. The technology is also reducing operational costs and helping telecom companies achieve greater margins with increased efficiency. Acadgild provides case studies on telecommunications companies that provide internet, phone, TV streaming and movie streaming services. These case studies help learners find models that can effectively predict customer behaviour using clustering techniques and deep learning.

Supply Chain

Demand Forecasting

Planning Management

Managing a supply chain has never been easy. Thanks to machine learning, however, we can now use algorithms to discover patterns in demand and other factors to ensure that there is never a shortage of goods and products. Algorithms deal with query data in an iterative fashion and improve the accuracy of predictions greatly. Acadgild covers various case studies on demand forecasting, planning management, etc.

Modular Projects

Develop Banking Application Using Python





Exception Handling

Apply Regression techniques to a European Soccer Database and give soccer players an overall rating based on attributes like ‘crossing’ and ‘finishing’. Learn to use Python, Numpy, Pandas, Scipy, Scikit, and Regression models.

Wrangling and Visualizing Meteorological Data







Data Cleaning

Data handling

Data Manipulation

Data Visualization

Use popular Data Cleaning libraries in Python like Pandas, Numpy, Matplotlib, etc., to Clean and Visualize meteorological data. Practice Data Handling, Manipulation and Visualization in this project.

Rate Soccer Players using Regression Models







Apply Regression techniques to a European Soccer Database and give soccer players an overall rating based on attributes like ‘crossing’ and ‘finishing’. Learn to use Python, Numpy, Pandas, Scipy, Scikit, and Regression models.

Estimate the Earnings of a Person using Classification Models









Run Census Bureau Data through Classification algorithms to estimate the salaries of individuals in the database. Learn to use Python, Numpy, Pandas, Matplotlib, Seaborn, Scipy, Scikit and Classification algorithms.

Predict How Various Stocks Will Perform using Clustering Models









Use Clustering algorithms to determine how different stocks will perform and how they correlate with each other based on Stock Market Data. Learn to use Python, Numpy, Pandas, Matplotlib, Seaborn, Scipy, Scikit, Clustering models.

Forecast Performance of Stocks using Time Series Models







Time Series Algorithm

Apply the Arima forecasting model to predict how stocks of various companies will perform. Learn to use Python, Numpy, Pandas, Matplotlib, Seaborn, Scipy, Scikit, and Time Series algorithms.

Classify Images using Deep Learning






Neural Network


Preprocess images and train a Convolutional Neural Network on all samples. The dataset will contain images of airplanes, dogs, cats and other objects. Learn to use Python, Numpy, Pandas, Scikit, Matplotlib, Neural Networks, CNN.

Generate TV Script using Deep Learning






Neural Network


Create a model that uses description of TV show episodes to generate a script for a new episode. Script your own episode of the Simpsons using RNNs and descriptions of episodes from all 27 seasons. Learn to use Python, Numpy, Pandas, Scikit, Matplotlib, Neural Networks, RNN.

One Simple Pricing


Billed once

  • Working time 24/7 all days
  • Free Tea & Coffee
  • Max 15 team members
  • Superfast wifi
  • Free Kitchen

Why Invest In A Data Science Career

The field of data science is thriving as it is proving to be effective not just across industries but also across departments within organizations.

Acadgild will transform you into a Data Scientist by delivering hands-on experience in Statistics, Machine Learning, Deep Learning and Artificial Intelligence (AI) using Python, TensorFlow, Apache Spark, R and Tableau. The course provides in-depth understanding of Machine Learning and Deep Learning algorithms such as Linear Regression, Logistic Regression, Naive Bayes Classifiers, Decision Tree and Random Forest, Support Vector Machine, Artificial Neural Networks and more. This 24 weeks long Data Science course has several advantages like 400 total coding hours and experienced industry mentors. This is the biggest advantage for understanding real world use cases and scenarios and applying theory in practice. Acadgild also ensures that real-time projects and case study discussions are facilitated to enhance learning. Icing on the cake is the lifetime access to our e-learning dashboard. This means you can log in and learn anytime about the latest technologies in the market.


In-Demand Skills

6 out of 10 developers are gaining or looking to gain skills in machine learning and deep learning.

Wide Use of Big Data

Well Paying Jobs

Data scientists make around $ 112,000 on average.

Data-Driven World

Shortage Of Data Scientists

There is a huge shortage of data scientists world over.

Here’s what people are saying about the course

You will emerge a highly skilled IT professional

Acadgild emphasizes active involvement of the students, and practical application of concepts learned in sessions. This course is worth the money and time. You will emerge a more confident, and highly skilled IT professional.

Project Manager (PMO-Delivery) at Deloitte

The support team at Acadgild was great

Learning at Acadgild was a good experience. They teach both theory and implementations of concepts. The support team at Acadgild was great. I had a great overall experience.

Senior Data Engineer at Brillio

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Customers served!

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Course Report

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Frequently Asked Questions

How is the experience of doing 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 Data Science 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 pre-requisites for this course?

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

What are the hardware and software requirements?

• Microsoft® Windows® 7/8/10 (32- or 64-bit).
• 8GB RAM minimum, 16 GB RAM (recommended).
• i5 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 will 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 and 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 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 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.

Case studies