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Data Science Certification Program

11000.00 INR 20000.00 INR

Data Science Certification Program

Course Information

Our comprehensive Data Science Guide has been specially designed to assist you in learning the concepts of data science from scratch. To begin with, the certification will assist you in getting started with some prerequisites before starting learning data science. This course will teach the mathematics concepts that are used in data science for eg: probability, statistics, etc, then cover the fundamentals of Python with data types, operators, control flows, and functions.

You will learn how to apply 360-Degree Python and machine learning concepts and principles in the data science domain. You will learn to use Python to analyze data, do statistical analysis, develop meaningful data visualizations, and forecast future trends. This course offers lectures designed specifically for people interested in data analysis, Python, and the R programming language.

Data Science Certification Overview

Data Science Certification teaches both novice and experienced users how to apply the most well-known and powerful machine learning algorithms. After certification, you will be industry-ready for the role of data scientist, data visualizer, or data analytics engineer.

The certification will help you get familiar with Python fundamentals and advanced Python with data types, operators, functions, scripting, and libraries, and start using Python for data science. You will also be able to systematically master the necessary concepts of mathematics, statistics, probability, and hypothesis testing.

Machine learning is typically explained using complex mathematical principles. This guide, however, cuts through the math and makes it easy for you to learn how machine learning algorithms work.This course will teach you the strengths and weaknesses of today’s most preferred machine learning algorithms. You’ll also learn the best algorithm to apply in real-world situations. 

You will gain hands-on experience with TensorFlow and its different data types, and learn how to use frameworks such as Keras for building and training models. The guide covers essential deep learning concepts including convolutional and recurrent neural networks, autoencoders, and data augmentation techniques, culminating in a hands-on project to solidify your learning.

Scope of Data Science

  • A recent analysis projects a 27.6% increase in demand for data scientists and their comprehension of big data by 2026.
  • As data science integrates statistics and predictive analysis, there will be a greater need for data scientists in the IT industry.
  • Because data scientists may work from anywhere, at any time, and at their own pace, the field offers both independence and flexibility.
  • To make sense of the collected data, data science employs interdisciplinary methods like deep technologies and machine learning. This aids in the creation of programs that are tailored to the needs of the client.
  • The area of data science is always expanding, and the dynamic nature of data has an effect on data scientists' careers.

    Course Content

  • Algebra (vectors and matrices) in Mathematics

    Introduction

    Content of this course

    What is a vector, its properties and what are unit vectors

    Linear combination of vectors (independent and dependent)

    What is a matrix

    Transpose of a matrix

    Determinant of a matrix

    Adjoint of a matrix

    Eigenvectors and Eigenvalues of a matrix

    Diagonal of a matrix

  • Ace Calculus 1 in 9 Hours The Complete Course

    Introduction

    Review on Precalculus

    Limits and Continuity

    Differentiation

    Derivatives of Transcendental Functions

    Limits - Indeterminate Forms

    Applications: Rates of Change

    Applications: Differential Equations

    Applications: Mean Value Theorem

    Applications: Linear Approximation

    Applications: Curve Sketching

    Applications: Optimization Problems

    Conclusion

  • Learn the fundamental tools of probability and combinatorics.

    Unveiling Uncertainty: Exploring Probability Basics and Random Experiments

    Unlocking the World of Combinations and Permutations in Combinatorics

    Data Analysis with Google Colab: Central Tendency & Probability

    Simulating Randomness: Sampling from Distributions

    Get Your Data Ready to Shine: Data Cleaning and Preprocessing Fundamentals

  • Data Science-Fundamentals of Statistics

    1.Introduction-Data and Statistics

    2.Measure of Central Tendency

    3.Measure of dispersion

    4.Shape of data_measure of Skewness

    5.Measures of relatioship -Correlation and Regresson Analysis

    6.Basic probability and Bayes theorem

    7.Discrete probability distribution-Binomial-Poisson

    8. Continuous probability distribution-normal distribution

    9.Rates-Ratio-OddRatio(OR)

    10.screenig test(Confusion Matrix)

  • Statistics and Hypothesis Testing

    Introduction to Statistics

    Introduction to Descriptive Statistics

    Introduction to Basic and Conditional Probability

    Introduction to Inferential Statistics

    Introduction to Hypothesis Testing

  • Microsoft Excel 365 - Beginner to Advanced Level

    Lets Start with Excel Fundamentals

    Mastering Mathematical Functions

    Mastering Logical Functions Use

    Data Filter and Sorting in Excel

    Conditional Formatting in Excel

    Mastering Text Functions and Tools

    Mastering Chart Presentation in Excel

    Mastering Pivot Table and Power Pivot

    Financial Functions and What if Analysis Tools

    Data Grouping and Linking

    How to Print Spreadsheet Efficiently

    Protect your Data in Excel

    Date and Time Management

    Mastering Lookup and Reference Functions

    Mastering Macro Recording Feature

    Resource Files

  • Microsoft Excel - Create Dashboards and Master Power Query

    Introduction

    Excel Functions

    Power Query

    Dashboard Tools

    Creating the Dashboard

    Design

  • Advanced Excel Tricks for Power Users: Boost your Skills

    Introduction

    Neat Tricks to Supercharge your Productivity

    Using Wildcards with Formulas

    The Awesome SUMPRODUCT Function - The Swiss Army Knife of Functions

    Advanced PivotTable Tricks

    The Hidden Power of the MOD Function

    The Magic of the INDIRECT Function

    The Incredible INDEX Function

    COUNTIF Function - A Mega Useful Excel Function

    Introducing Custom Number Formatting

    Advanced Custom Formatting Examples that Users did not know was Possible

    Advanced Chart Tricks for Dynamic Interactive Charts

  • The Complete Python Bootcamp - Automate Anything!

    Introduction

    Installation & Set Up

    Things to know before we get started!

    Numbers

    Strings

    Lists

    Dictionaries

    Tuples

    Sets

    Control Flow

    Loops

    Functions

    Adding interaction with input

    Project 1 - Number guessing Game

    Project 2 - Shopping list

    Project 3 - Fitness App

    Project 4 - Rock, Paper, Scissors

    Project 5 - Hangman

    Project 6 - Noughts & Crosses

    Object Oriented Programming

    Modules and Packages

    Errors and exceptions handling

    The Las Vegas Casino Project - 21

    Python Decorators

    Python Generators

    Introduction to Web Scraping with Python

    Creating a User Interface - Introducing TKinter

    Restaurant Management System Project

    EXTRA SECTION - Creating Dashboards using Plotly and Dash

    Working with Databases, CSV files and PDF files

    The Collections Module

    Regular Expressions (regex)

    Timing your Python Code

    Congratulations

  • Python Data Science Course With Numpy, Pandas and Matplotlib

    Course Introduction and Table of Contents

    Introduction to Python, Pandas and Numpy

    System and Environment Setup

    Python Strings

    Python Numbers and Operators

    Python Lists

    Tuples in Python

    Sets in Python

    Python Dictionary

    NumPy Library Introduction

    NumPy Array Operations and Indexing

    NumPy Multi-Dimensional Arrayss

    Introduction to Pandas Series

    Introduction to Pandas Dataframes

    Pandas Dataframe conversion and drop

    Pandas Dataframe summary and selection

    Pandas Missing Data Management and Sorting

    Pandas Hierarchical-Multi Indexing

    Pandas CSV File Read Write

    Pandas JSON File Read Write

    Pandas Concatenation Merging and Joining

    Pandas Stacking and Pivoting

    Pandas Duplicate Data Management

    Pandas Mapping

    Pandas Groupby

    Pandas Aggregation

    Pandas Binning or Bucketing

    Pandas Re-index and Rename

    Pandas Replace Values

    Pandas Dataframe Metrics

    Pandas Random Permutation

    Pandas Excel sheet Import

    Pandas Condition Selection and Lambda Function

    Pandas Ranks Min Max

    Pandas Cross Tabulation

    Graphs and plots using Matplotlib

    Matplotlib Histograms

    Source Code

  • R Programming for Data Science

    Data Science Overview

    R and RStudio

    Introduction to Basics

    Vectors Data Structure

    Matrices Data Structure

    Factors Data Structure

    Data Frames Structure

    Lists Data Structure

    Relational Operators

    Logical Operators

    Conditional Statements

    Loop Structures

    Functions in R

    Packages in R

    The apply Family – lapply

    The apply family – sapply & vapply

    Useful Functions in R

    Regular Expressions

    Dates and Times

    Getting and Cleaning Data

    Data Manipulation with dplyr

    Plotting Data in R

    COVID19 Analysis: Case Study

  • The Complete MySQL Bootcamp: Zero to Hero SQL Skills

    Introduction

    How to Install MySQL

    Basic Understanding of SDLC

    Understanding the Business Requirements and Project Architecture

    Dumping the data in the Staging Layer of the Database using Tableau Prep

    Creating and Loading Customer Dimension Table

    Creating and Loading Other Dimension and Fact Tables

    Creating the Customer Dimension Table with Customer Type

    Loading the Final Layer with all the relevant constraints

    Functions in MySQL

    Group By for Reporting

    View in MySQL

    Subqueries in MySQL

    Common Table Expressions (CTEs)

    Window Functions

    End to End Data Flow Prep (Data Engineering) Plus Quick Revision

    Understanding the Slowly Changing Dimensions (SCDs)

    Loading Sales Data Coming from External Application

    Performance Tuning using Indexes

    Regular Expressions (REGEXP) in MySQL

    Miscellaneous Topics

    Data Warehousing Concepts

    Deployment to Production Environment using Git and GitHuB

  • Data Science: Exploratory Data Analytics (EDA) Techniques

    Introduction to Data Science Practical Approach

    Data Science Practical Session Part1

    Data Science Practical Session Part 2

  • Python for Data Analysis: Students and Professionals

    Welcome to the Course

    Load and Write Data

    Data Types and Formatting

    Data Manipulation

    Join Data Operations

    Data Aggregation and Feature Engineering

    Time Intelligence

    Data Visualization with matplotlib and seaborn

    Capstone Projects

    CONGRATULATIONS!!!

  • Data Analytics: Python Visualizations

    Introduction

    Matplotlib and Seaborn – Libraries and Techniques

    Advanced Visualisations using Business Applications

    Working with the Beautiful and Powerful Bokeh Library

  • R for Data Analysis: Students and Professionals

    Welcome to the Course

    Load and Write Data in R

    Data Types and Formatting

    Data Manipulation

    Join Data Operations

    Data Aggregation and Feature Engineering

    Time Intelligence

    Data Visualization with ggplot2

    Capstone Projects

    CONGRATULATIONS!!!

  • Tableau Course - Basics to Advanced

    Tableau An Introduction

    Tableau - Business Case 1 (Calculated Field and Functions (Left, if else))

    Tableau - Business Case 2 (Extract vs Live and Union)

    Tableau - Business Case 3 (Data from heterogeneous source - Crossdatabasejoin)

    Tableau - Business Case 4 (Data Blending)

    Tableau - Business Case 5 (Bins and Parameters)

    Tableau - Business Case 6 (Filters, Aggregation, Granularity and Date Dimension)

    Tableau - Business Case 7 (Sets and Parameters)

    Tableau - Business Case 8 (Animations in Tableau)

    Tableau - Level of Details

    Tableau - Table Calculations

    Tableau - Background Image

    Misc Topics

    Custom Charts in Tableau

    Tableau Dashboard Techniques

    Tableau Online

  • Microsoft Power BI Course: Master DAX and Dashboard Creation

    Course Project

    Intro to POPWER BI Desktop

    How to Connect and Transform Data in Power Query

    Creating Data Modeling

    How to create a Date Table using DAX

    Understand Calculated Column in Power BI

    Understand Measures In Power BI

    Context Transition

    Simplify DAX with Variables

    Understand the default Filters in Power BI Desktop

    Visualization ( Detail Dashboard)

    Visualization (Dashboard with forecasting)

    What-If Analysis Dashboard

    What-If Analysis Dashboard with Top-5

    Returned Dashboard

    Dashboard with Image Ranking

    Drug Sales Dashboard Inspiration

  • DataSimple Python Data Analysis Bootcamp - Pandas, Seaborn and Plotly

    Data Analysis Intro

    Univariate Analysis

    Bivariate Analysis

    Data Analysis: Wrangling, Clean and Preprocessing

    Advanced Data Analysis

    Data Story Telling

  • Mastering Machine Learning from Scratch

    Introduction to the Course

    Why Machine Learning

    Process of Learning from Data

    Machine Learning Methods

    Data Preparation and Preprocessing

    Machine Learning Models and Optimization

    Building Machine Learning Model from Scratch

    Overfitting, Underfitting and Generalization

    Machine Learning Model Performance Metrics

    Dimensionality Reduction

    Deep Learning Overview

    Hands-on Machine Learning Project Using Scikit-Learn

    OPTIONAL Section- Mathematics Wrap-up

  • Recommender System: Recommender System with Machine Learning

    Introduction

    Motivation for Recommender System

    Basic of Recommender System

    Machine Learning for Recommender System

    Project 1: Song Recommendation System using content based filtering

    Project 2: Movie Recommendation System using collaborative filtering

  • Fundamentals of Reinforcement Learning in Python Course

    Introduction to Course and Instructor

    Motivation Reinforcement Learning

    Terminology of Reinforcement Learning

    GridWorld Example

    Markov Decision Process Prerequisites

    Elements of Markov Decision Process

    More on Reword

    Solving MDP

    Value Approximation

    Temporal Differencing-Q Learning

    TD Lambda

    Project Frozenlake (Open AI Gym)

  • Deep Learning Course with Hands-On Project

    Deep Learning

  • Deep Neural Networks using Python for Absolute Beginners

    Introduction to Deep Learning

    Basics of Deep Learning

    Deep Learning

    Optimizations

    Final Project

  • NLP-Natural Language Processing in Python

    Introduction

    Introduction(Regular Expressions)

    Meta Characters(Regular Expressions)

    Pattern Objects(Regular Expressions)

    More Meta Characters(Regular Expressions)

    String Modification(Regular Expressions)

    Words and Tokens(Text Preprocessing)

    Sentiment Classification(Text Preprocessing)

    Language Independent Tokenization(Text Preprocessing)

    Text Nomalization(Text Preprocessing)

    String Matching and Spelling Correction(Text Preprocessing)

    Language Modeling

    Topic Modelling with Word and Document Representations

    Word Embeddings LSI

    Word Semantics

    Word2vec(Optional)

    Need of Deep Learning for NLP(NLP with Deep Learning DNN)

    Introduction(NLP with Deep Learning DNN)

    Training(NLP with Deep Learning DNN)

    Hyper parameters(NLP with Deep Learning DNN)

    Introduction(NLP with Deep Learning RNN)

    Mini-project Language Modelling(NLP with Deep Learning RNN)

    Mini-project Sentiment Classification(NLP with Deep Learning RNN)

    RNN in PyTorch(NLP with Deep Learning RNN)

    Advanced RNN models(NLP with Deep Learning RNN)

    Neural Machine Translation

  • Low Code Machine Learning and Deployment in Python: Hands On Training

    Introduction

  • Integrating DevOps Tools into a CI/CD Pipeline in AWS

    Introduction

    Provisioning AWS Infrastructure with Terraform

    Launching and Configuring Jenkins

    Launching and Configuring SonarQube

    Launching and Configuring Ansible

    Creating and Configuring the Application Environmet

    Monitoring the Infrastructure

    Conclusion

  • Recommender Systems Complete Course Beginner to Advance

    Introduction

    Recommender Systems with Machine Learning

    Deep Learning for Recommender Systems: An Applied Approach

  • Fraud Detection in Python

    Introduction to the course

    Introduction to Fraud Detection

    Training a Supervised Machine Learning Model

    Performance Metrics for Fraud Detection

    Optimal Model Selection

    Strategies for Improving Model Performance

  • Speech Recognition A-Z - Hands-on Course

    Course Introduction

    Lesson 1: Speech and its Types

    Lesson 2: Automated Speech Recognition

    Lesson 3: Speech to Text

    Lesson 4: Text to Speech

    Lesson 5: Multilingual Speech Processing

    Lesson 6: Speech Translation

    Lesson 7: Spoken Dialog System

    Lesson 8: Voice Conversion

    Course End Project

  • Learn Data Science from Scratch

  • Data Science and Machine Learning Interview Questions Using R

Tools to Master

Python Python
R Programming R Programming
SQL SQL

Course Certification

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