India’s Best Online Data Science Course With Certificate & Placement

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Best Online Data Science Course in India

4.8 (1134 Reviews)

Want to kickstart and build a career in one of the most demanded fields today? Enroll in India’s most comprehensive and best online course on data science.

Throughout the course, you will learn data science online from the industry’s top mentors, from basic to advanced modules. The full course covers six months of comprehensive data science training with Python, Machine Learning, Deep Learning, as well as Data Analytics. We also assist you in job placement and career growth.

Online Data Science Course

Upcoming Batch Details

Duration Timings
(Mon - Sat) 4 Months 8:00 AM to 9:00 AM
(Mon - Sat) 4 Months 6:00 PM to 7:00 PM
(Mon - Sat) 4 Months 7:00 PM to 8:00 PM
(Mon - Sat) 4 Months 8:00 PM to 9:00 PM

Course Fees

₹17,699/-

(including GST)

-₹27,700/-

Experience our demo class for just Rs. 499/- (refundable). Register now!

About Our Online Data Science Certification Course

This is the era of digital transformation where every organization is digitizing itself for accelerated growth. Data offers meaningful information and insights that aid in business growth. The demand for skilled data scientists has been increasing over the years in India and globally.

Today, there are modern tools and technologies but there is a lack of data scientists. The demand for data scientists is huge. It is your time to make the most out of this demand by becoming a skilled data scientist and analyst.

For this, WsCube Tech is offering India's best online data science course that makes you skilled in analyzing the data treasure. You will learn from an industry expert and work on large sets of data. During this 6-month training, you will be on your way to becoming a top-class data scientist and exploring high-paying job opportunities.

WsCube Tech certificate

What You’ll Learn in This Data Science Online Training?

Well-structured & comprehensive curriculum designed according to latest trends and industry standards!

  • Introduction to Python and its features
  • Installing Anaconda /Jupyter
  • Variables Data Type and Object
  • Difference between Compiler and Interpreter
  • Basic Data Types of Python
  • Comments in Python
  • Operators
  • Types of Operators
  • Print method and its argument
  • Different print formatting
  • Input method
  • Typecasting

  • Conditional Statements
  • If elif and else statements
  • Nested if
  • Exercise for if else condition
  • Loops
  • For loop and range function
  • While loop
  • Break and continue statements
  • Nested loops in Python
  • For-else and while-else statement
  • Exercise: Conditional and loop-based questions

  • Introduction to List
  • Indexing on List
  • Slicing on List
  • List Methods I– Append, Extend, Insert
  • List Methods II– Pop, Remove, Clear
  • List Methods III– Sort()
  • List Methods IV– Reverse
  • List Methods V–Count, Index
  • Using Condition statement in list
  • Using Loops in list
  • Exercise for list and assignment

  • Introduction to Tuples
  • Tuple Methods– Index, count
  • Tuple Exercises

  • What is Dictionary
  • Dictionary Methods I– Clear, copy, Fromkeys
  • Dictionary Methods II– get, update,
  • Dictionary Methods III– Pop, popitem, setdefault
  • Dictionary Methods IV– key, values, items
  • Dictionary Methods V–setdefaults

  • Strings
  • Indexing on Strings
  • Slicing on Strings
  • Immutable Strings
  • String Methods I– upper, lower, title, capitalize, swapcase
  • Strings Methods II– strip, find, index, isalnum
  • Strings Methods III– startswith, endswith, split, replace
  • Strings Methods IV– isalpha, isalnum, isupper, islower
  • Using Condition statement with string
  • Using Loops with string
  • Exercises

  • What is Set?
  • Set Methods I– add, copy
  • Set Methods II– difference, difference update, symmetric difference, symmetric_difference_update
  • Set Methods III– union, intersection, intersection_Update
  • Set Methods IV– isdisjoint, issubset, isuperset,
  • Set Methods V–pop, clear, remove

  • Different types of functions
  • User-defined functions
  • Creating functions with and without arguments
  • Positional and default arguments
  • Return and Non-Return type function in Python
  • Recursive functions
  • Unpacker Object in Python
  • *args and **kwargs function in python
  • Scope of variables - local and global
  • Anonymous Functions– lambda
  • Exercise– Functions, and Recursion

  • Importing modules
  • Using modules I– math, random
  • Using modules II– itertools, collections
  • Inbuilt Functions I– map, reduce, filter
  • Inbuilt Functions II– enumerate, eval, zip,
  • Exercise– Inbuilt functions and libraries

  • Working with files
  • Opening and closing a file
  • Modes of opening a file
  • Reading, writing, and appending to a file
  • Handling text files using readlines, read, tell, seek methods
  • Handling CSV files in Python

  • What is an Exception?
  • Understanding try-except-else block of code
  • Types of exceptions I– ZeroDivisionError, TypeError, NameError
  • Types of exceptions II– ValueError, IndexError
  • Handling multiple Exceptions
  • Raise keyword to generate exceptions

  • Understanding class and objects
  • Self keyword
  • Creating a class in Python
  • Understanding constructor
  • Difference between a constructor and a method
  • Types of variable– Instance and static
  • Creating, accessing, modifying, and deleting Instance variables
  • Creating, accessing, modifying, and deleting Static variables
  • Types of Methods - Instance, Class, and Static methods
  • Getter and Setter methods
  • Understanding Inheritance
  • super method
  • Types of Inheritance - single, multilevel, multiple, hierarchical
  • Polymorphism and method overriding
  • Encapsulation
  • Exercise – OOPC

  • Overview of Artificial Intelligence (AI), Machine Learning, and Data Science
  • Understanding the varied applications of Data Science
  • Different sectors using Data Science

  • Understanding Statistics
  • Understanding data, sample, and population
  • Types of data– Qualitative and Quantitative
  • Descriptive Statistics
  • Uni-variate Data Analysis– Measure of Central Tendency
  • Mean, Median and Mode
  • Uni-variate Data Analysis– Measure of Dispersion
  • Range, Variance, Standard Deviation
  • Bi-variate Data Analysis– Covariance and Correlation
  • Inferential Statistics
  • Central Limit Theorem
  • Random Variable and different types of random variable
  • Probability Distribution Functions
  • Normal Distribution
  • Binomial and Poisson Distributions
  • Skewness and different types of skewness
  • What is Hypothesis Testing?
  • Null and Alternate Hypothesis
  • P-value, Level of significance
  • Confidence Level and Confidence Interval
  • One Sample Z-test
  • learner’s T-test
  • Chi Square Test
  • Exercise– Statistics

  • Introduction to NumPy
  • Features of NumPy
  • Create NumPy Array
  • Different ways to create NumPy array
  • Numpy Custom Array Creation using zeros, ones, linspace, etc.
  • NumPy Array Indexing
  • NumPy 1D, 2D, and 3D Indexing
  • NumPy slicing
  • NumPy advanced indexing and slicing
  • Generating NumPy arrays with random values
  • NumPy Array Broadcasting
  • NumPy Array Iterating
  • NumPy Array Manipulation
  • NumPy Arithmetic Operation
  • NumPy Statistical Function
  • numpy.amin() and numpy.amax()
  • numpy.ptp(), numpy.percentile()
  • numpy.median(), numpy.mean()
  • numpy.average(), Standard Deviation
  • Variance
  • NumPy Random
  • What is Random Number
  • Generate Random Number
  • Generate Random Float
  • Generate Random Array
  • Generate Random Number From Array
  • Random Data Distribution
  • What is Data Distribution?
  • Random Distribution
  • Random Permutations
  • Random Permutations of Elements
  • Shuffling Arrays
  • Generating Permutation of Arrays
  • Seaborn
  • Visualize Distributions With Seaborn
  • Distplots
  • Import Matplotlib
  • Import Seaborn
  • Plotting a Distplot
  • Plotting a Distplot Without the Histogram
  • Normal (Gaussian) Distribution
  • Normal Distribution
  • Visualization of Normal Distribution
  • Binomial Distribution
  • Visualization of Binomial Distribution
  • Difference Between Normal and Binomial Distribution
  • Poisson Distribution
  • Visualization of Poisson Distribution
  • Difference Between Normal and Poisson Distribution
  • Difference Between Poisson and Binomial Distribution
  • Uniform Distribution
  • Visualization of Uniform Distribution
  • Logistic Distribution
  • Visualization of Logistic Distribution
  • Difference Between Logistic and Normal Distribution
  • Multinomial Distribution
  • Exponential Distribution
  • Visualization of Exponential Distribution
  • Relation Between Poisson and Exponential Distribution
  • Chi Square Distribution
  • Visualization of Chi Square Distribution
  • Rayleigh Distribution
  • Visualization of Rayleigh Distribution
  • Similarity Between Rayleigh and Chi Square Distribution
  • Pareto Distribution
  • Visualization of Pareto Distribution
  • Zipf Distribution
  • Visualization of Zipf Distribution

  • Introduction to Pandas
  • Understanding Series in Pandas
  • Creating Series using– NumPy array, list, tuple, from a .csv/excel file
  • Series methods– mean, sum, count, etc.
  • Series indexing and slicing using– iloc and loc
  • Reading a .csv, .excel files using Pandas– read_csv, read_excel
  • Understanding DataFrame in Pandas
  • Creating DataFrame using NumPy array, list, tuple, from a .csv/excel file
  • Head, tail, and sample methods for DataFrame
  • DataFrame indexing and slicing using– iloc and loc
  • Accessing column values from a DataFrame
  • Set DataFrame index, sort index, and values
  • DataFrame query
  • Find unique values for a column in DataFrame
  • Group by method
  • Data wrangling methods I– merge, append, concat
  • Data wrangling methods II– map, apply, applymap
  • Data cleansing I– rename columns, rearrange columns
  • Data cleansing II– remove null values, fill null values
  • Data cleansing III– drop rows, drop columns
  • Handling datetime in Pandas
  • Pivot table

  • Introduction to Matplotlib visualization
  • Bar Chart
  • Line Chart
  • Scatter Chart
  • Pie Chart
  • Histogram
  • Boxplot
  • Subplots
  • Exercise– Matplotlib and Pandas

  • Introduction to Seaborn visualization
  • Countplot
  • Boxplot
  • Violinplot
  • Pairplot
  • Heatmap
  • Scatterplot
  • Plotting Geospatial maps using Plotly

  • Exploratory Data Analysis Overview
  • Project– EDA On Cardio Good Fitness Data
  • Project– Bank dataset EDA
  • Project– Used cars dataset EDA

  • Introduction to Machine Learning
  • Understanding different types of Learning– Supervised and Unsupervised Learning
  • Understanding Supervised and Unsupervised algorithms
  • Difference between Supervised and Unsupervised Learning

  • Splitting data into training and test datasets
  • Understanding the working and equation of Regression Analysis
  • Regression metrics– R2-score, MAE, MSE, RMSE
  • Implementation of Simple Linear Regression
  • Implementation of Multiple Linear Regression
  • Project– Heating and Cooling Load Prediction

  • Understanding Confusion Matrix
  • Understanding the concept of True positive, False Positive, True
  • Negative and False Negative
  • Classification Metrics– Accuracy, Precision, Recall, F1-Score
  • Bias Variance, Underfitting, and Overfitting

  • Understanding the working of Logistic Regression
  • Derivation of Sigmoid function
  • Implementation of Logistic Regression
  • Project– Diabetic patient Classification

  • Understanding the working of KNN
  • Algorithm of KNN
  • Implementation of KNN
  • Project– Social Network Ads Classification

  • Understanding the working of Decision Tree
  • Understanding Gini and Entropy criterion
  • Implementation of Decision Tree Classification
  • Understanding the working of Random Forest Classification
  • Concept of Bootstrapping
  • Implementation of Random Forest Classification
  • Project– Iris Flower Classification
  • Project– Placement Prediction

  • Understanding the working of Naive Bayes
  • Implementation of Naive Bayes Classification
  • Project– News Classification

  • Understanding the working of K-Means Clustering
  • Understanding of Elbow method to find the optimal number of clusters
  • Implementation of K-Means Clustering
  • Project– Shopping dataset Clustering

  • Understanding the working of PCA
  • Understanding Eigen values and Eigen vectors
  • Implementation of PCA

  • Difference between Bagging and Boosting
  • Understanding working of AdaBoost
  • Implementation of AdaBoost
  • Understanding working of XGBoost
  • Implementation of XGBoost

  • Introduction to NLP
  • Removing Stop Words, Stemming, Lemmatization
  • Count Vectorizer and Tf-Idf
  • Project– Spam vs. ham Email Classification

  • Reading and displaying an image using OpenCV
  • Image Transformation operations
  • Filtering and Thresholding
  • Erosion and Dilation
  • Object Detection using Haar Cascade Files– Face and car Detection
  • Project– Clustering colors in images

  • Introduction to Neural Network
  • What is a Neuron?
  • Working of a Neuron
  • Perceptron Model
  • Concept of Hidden layers and Weights
  • Concept of Activation Functions, Optimizers, and Loss Functions
  • Equation of a General Neural Network
  • Understanding Backpropagation

  • Introduction to TensorFlow
  • Importing TensorFlow
  • Using TensorFlow on Colab
  • What is a tensor?
  • Indexing and Slicing
  • Tensorflow basic operations

  • Understanding different Activation Functions
  • Linear, Sigmoid, Tanh, Relu
  • Understanding different Loss Functions
  • MSE, Binary CrossEntropy, etc.
  • Understanding different Optimizers
  • Gradient Descent, Adam, etc.

  • Implementation of a Neural Network
  • Implementation of ANN for Regression
  • Implementation of ANN for Classification
  • Project– Customer Churn Modelling

  • Understanding CNN (Convolutional Neural Network)
  • Understanding the Convolution process
  • Concept of Filter, strides
  • Pooling Layer
  • Fully Connected Layer
  • Project– MNIST Image Classification
  • Project– Fashion MNIST Image Classification

  • MNIST Image Classification
  • Fashion MNIST Image Classification
  • Customer Churn Modelling
  • Spam vs Ham Email Classification
  • HR Analytics Classification
  • Big mart Sales Prediction
  • Bank Loan Prediction

Why Learn Data Science Online With WsCube Tech?

Training by Industry Experts

Training by Industry Experts

The entire course is covered by a data science and machine learning expert having 11+ years of experience. You learn everything in-depth and in an easy-to-learn way.

Dedicated Doubt-Clearing Sessions

Dedicated Doubt-Clearing Sessions

We understand that learners can have doubts related to any topic. To help you learn effectively, we arrange dedicated doubt-clearing sessions for you.

Interactive Live Classes

Interactive Live Classes

We conduct regular live classes that are highly interactive and engaging. You can unmute and discuss with the mentor. No pre-recorded lessons are included.

Hands-on Projects

Hands-on Projects

Another reason that makes it the best online course on data science is that we offer practical exposure to learners. You get to work on real and live projects during the training.

100% Job Assistance

100% Job Assistance

It is a data science online course with placement, which means after the training and projects, we help you in getting hired at good companies.

Data Science Certification

Data Science Certification

Along with the right skills, you also get certified by India’s leading IT India’s leading Edtech company. This is a data science online course with certificate so that you can easily explore job opportunities.

Wscube Tech owner Kushagra bhatia

“It's time for you to future-proof your career!”

“We know that we are influencing the foundations of your future, and we take this responsibility very seriously. With WsCube Tech, I ensure that you always get top-class training backed by practical projects and future prospects. Wishing you a successful & future-proof career!”

Kushagra Bhatia, Founder, WsCube Tech

Learners Love WsCube Tech’s Data Science Certification Course

We are proud to have positively influenced the career foundations for thousands of learners across India and Asian countries.

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Data Science Full Course FAQs

Data science is the field that brings together statistics, scientific methods, data analysis, as well as machine learning (ML), and artificial intelligence (AI). The purpose of data science is to find value from heaps of data from websites, customers, smartphones, sensors, software, etc.

The job profile in data science is usually a data scientist. A data scientist’s work is to utilize his skills for analyzing data, cleansing, aggregating, and manipulating it. This data analysis helps in making data-driven business decisions and uncovering unknown patterns.

The primary subjects covered in the data science and analytics courses are Python, Machine Learning, Deep Learning, Data Analytics, and Artificial Intelligence (AI).

While it is not necessary to have professional knowledge of programming or technical stack, but if you have some basic knowledge, it is an add-on and helps you learn data science fast.

In order to become a data scientist, you must have the right skills and command of several subjects and technologies. These include Python, data analytics, machine learning, deep learning, etc. To start with, you must enroll in the best data scientist certification course. Then you can get placement or get hired by top companies in the country.

The job role of a data scientist is to collect large amounts of data and analyze it intelligently. With the right skills, you can implement your analytics to solve critical challenges for businesses, customers, and other problems.

Since it is still one of the unexplored IT fields in India, many people wonder:

  • Is there a demand for data scientists in India?
  • Is it hard to get a data science job in India?

The answer is that it is one of the top careers in the country and abroad today. Skilled data scientists are in high demand. Startups to SMBs to large organizations are looking for qualified candidates in their teams.

There are dedicated data analytics companies providing services to other organizations. By doing data science with Python course and practicing analysis of data, you can grab these opportunities.

A few of the top companies hiring data scientists include LensKart, Microsoft, Accenture, Oracle, Pinterest, Slack, Intel, Uber, Ernst & Young (EY), IBM, Aditya Birla Group, etc.

You can enroll in our online content writing course and gain the right skills to start your career without any prior experience.

The average data scientist salary in India is INR 7.00 LPA. A fresher’s salary starts at INR 5 LPA, whereas someone with 1-4 years of experience can make INR 6 to 10 LPA. Data scientists with 5+ years of experience make more than 11 LPA.

Yes. You will get the data science certificate on course completion.

Not to worry. In case you miss a live class, you will get the recording of the class, which you can watch according to your time. For any doubts, you can ask the mentor in the next class or the doubt-clearing session.

Yes. On completion of the course, we will prepare you for the interview. Following preparation, we will align your interviews with several top companies in the country and help you get placed.

Companies like Microsoft, IBM, Uber, Intel, Accenture, and Oracle are hiring data scientists in India at an average salary of INR 7 LPA.

It’s time for you to acquire the right skill set and grab the opportunities.

Enroll in India’s Best Data Science Certification Training Today!

  • Python Basic to Advanced
  • Object-oriented Programming
  • Data Analytics With Statistics
  • NumPy and Pandas
  • Seaborn and Plotly
  • Machine Learning
  • Natural Language Processing
  • Deep Learning
  • TensorFlow
  • Neural Networks
  • And a total of 35+ modules

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