Best Data Science Course in Indore (Training by Data Scientist)
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Data Science Course in Indore (By Top Training Institute)

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Learn data science from highly-qualified mentors and earn a professional certification from one of the most trusted institutes in India. WsCube Tech offers the best data science course in Indore, introducing you to the dynamic field of data analysis. While pursuing the course, you work on several hands-on projects, acquire practical skills, get round-the-clock support, and interact with industry practitioners.

Our experts have curated extensive courses that cover technologies such as Python, R, Tableau, Machine Learning, Spark, and more. Join the leading data science training institute in Indore now and improve your technical and non-technical skills.

Data Science Course in Indore

Upcoming Batch Details

Duration Timings
(Mon - Sat) 5 Months 8:00 AM to 9:00 AM
(Mon - Sat) 5 Months 6:00 PM to 7:00 PM
(Mon - Sat) 5 Months 7:00 PM to 8:00 PM
(Mon - Sat) 5 Months 8:00 PM to 9:00 PM

Course Fees

₹17,699/-

(including GST)

-₹27,700/-

Get Industry-Ready With The Best Data Scientist Course in Indore

Data Science offers ample professional opportunities worldwide. The subject focuses on extracting information from the massive dataset for actionable insights. It can be any data stored over the internet, from a person’s preferences and contact details to social media activities and whatnot.

Companies collect this raw data and add it to their data lake. However, they need a professional data scientist to structure and process unstructured data. Hence, it is no surprise that the demand for proficient data scientists has witnessed a great surge in the job market. However, there is a limited supply of skilled data scientists and our data science courses in Indore bridge that gap.

WsCube Tech is known for offering the most comprehensive and best data science course in Indore to learners from different walks of life. Our experienced mentors have curated an extensive curriculum that covers every topic from scratch.

Whether you are a freelancer, beginner, or working professional, our syllabus aligns with different learning goals. From data analysis and artificial intelligence to machine learning and more, our mentors cover every topic in detail during virtual and in-class training. You will also acquire essential data science skills, such as data collection, integration, extraction, statistical analysis, data mining, predictive analysis, and more.

Upon completing our data science training in Indore, you will receive a professional certificate from WsCube Tech, which is trusted by various leading companies. You will also get job assistance and the necessary professional guidance to kickstart a thriving career.

WsCube Tech certificate

Curriculum of Our Data Science Training in Indore

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 Makes WsCube Tech The Best Data Science Training Institute in Indore?

Training by Experts

Training by Experts

Get trained by experienced faculty members from top institutes and universities. We also have qualified data scientists in our teams who have worked with leading companies.

Interactive Live Classes

Interactive Live Classes

Learn with hundreds of people from different walks of life. Our mentors encourage group discussion, one-on-one interaction, and fun quizzes in live sessions.

Regular Assessments

Regular Assessments

We assess your learning, knowledge, and practical skills through hands-on projects. After evaluating your assignments, our mentors provide valuable feedback for further improvements.

Comprehensive Syllabus

Comprehensive Syllabus

The syllabus or curriculum of our data scientist course in Indore covers all depths and breadths of the field to help you become a master at all relevant concepts.

Placement Assistance

Placement Assistance

Upon completing the course, you will get all the assistance you need to find a suitable job in the field. Our experts will also help you prepare a strong resume and prepare for interviews.

Data Scientist Certification

Data Scientist Certification

We train you to be a certified data scientist ready to take on industry challenges and handle real-world projects. Hence, reward you with a professional certificate to validate your capabilities.

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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

What Our learners Are Saying!

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 Course Indore FAQs

Data Science is applied across most industries and is considered one of the most sought-after skills in the IT sector. It combines statistics, data analysis, scientific methods, artificial intelligence, and machine learning. The purpose of the field is to seek value from heaps of data collected from different sources.

A professional data scientist collects a large amount of data, gets insights, and analyzes it. They apply their analytical and technical skills to provide solutions to several business challenges.

They are responsible for finding valuable insights by analyzing massive datasets. So, their core tasks are to cleanse, aggregate, manipulate, and study data to help make data-driven decisions.

As an accomplished and skilled data scientist, you can apply for the following job roles:

  • Data Architect and Administrator
  • Data Mining Engineer
  • Data Analyst
  • Data Scientist
  • Team Lead- Data Engineer
  • Machine Learning Engineer
  • Business Analyst
  • Marketing Analyst
  • Statistician and Mathematician

You don’t need any specific academic degree or professional experience to sign up for our data science training in Indore. Freelancers, business owners, graduates, freshers, and working professionals from any field are welcome to join our community.

Our data science course covers all areas and aspects of the field, including:

  • Part-I: Python
  • PART-II: Data Analysis With Statistics And Visualization
  • Part-III: Machine Learning
  • Part-IV: Deep Learning, AI, Neural Network

Data science is surely a promising career option and will offer ample opportunities in the coming years. This versatile field allows you to work across multiple industries, such as finance, education, healthcare, information technology, and more. Moreover, it offers several secure and high-paying job vacancies, which you can select based on your interest and expertise.

The average salary of a beginner-level data scientist is ₹8 LPA, and with years of experience and expertise in the field, you can make around ₹35 LPA and more.

Our data science course spans 6 months.

Coding or programming is not a necessary skill to enrol in our data science courses in Indore. Having basic technical knowledge may be beneficial, but it doesn’t define your efficiency or learning capabilities.

The fee for our data scientist course in Indore is 5 months, which includes hands-on projects and job assistance.

Build Your Career in Data Science— the Fastest-Growing Technology Field!

Start learning now with WsCube Tech.

Book Your Free Class Now!

  • 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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WsCube Tech is the Vernacular Upskilling Edtech platform. By offering expert mentorship with our tech-powered courses in the first language of Indians, we equip learners with the skills to thrive in the global workforce.

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