Learning Path

  • Course 1

    Post Graduate Program in Data Science

    • Upcoming Batches
    • Curriculum
      Sl. No. Chapter
      Chapter - 1 Introduction to programming using Python
      Chapter - 2 Database Management System using My SQL
      Chapter - 3 Statistical Methods for Decision Making
      Chapter - 4 Exploring Data Analysis
      Chapter - 5 Supervised Learning - Regression
      Chapter - 6 Supervised Learning - Classification
      Chapter - 7 Unsupervised Learning
      Chapter - 8 Ensemble Techniques
      Chapter - 9 Data Visualization Using Tableau
      Chapter - 10 Data Visualization Using Google Data Studio
      Chapter - 11 Time Series Forecasting
      Chapter - 12 Deep Learning And Neural Network
      Chapter - 13 Text Mining And Sentimental Analysis

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  • Course 2

    Data Science with Python

    • Upcoming Batches
    • Curriculum
      Sl. No. Chapter
      1 Introduction to Data Science using Python
      2 Python basics
      3- Maths for DS-Statistics
      4- OOPs in Python
      5- NumPy Library In python
      6- Scipy for scientific computing
      7- Data manipulation
      8- Data visualization with Matplotlib
      9- Machine Learning using Python
      10- Supervised learning
      11- Unsupervised Learning
      12- Dimensionality Reduction
      13- Time Series Forecasting
      14- Project

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  • Course 3

    Machine Learning With Python

    • Upcoming Batches
    • Curriculum
      Sl. No. Chapter
      1 Introduction to Machine Learning using Python
      2 Introduction to Statistics & Probability
      3 Machine Learning-Supervised learning
      4 Machine Learning-UnSupervised learning
      5 Dimensionality Reduction-PCA Concept
      6 Text Mining And Sentimental Analysis
      7 Time Series Forecasting
      8 Industry Graded project

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  • Course 4

    Deep Learning Certification Training

    • Upcoming Batches
    • Curriculum
      Sl. No. Chapter
      Chapter - 1 Introduction to Deep Learning
      Chapter - 2 Neural Network
      Chapter - 3 Advantage of Deep Learning over Machine learning
      Chapter - 4 Understanding Neural Networks with TensorFlow
      Chapter - 5 Activation Functions
      Chapter - 6 Training a Perceptron
      Chapter - 7 TensorFlow code-basics
      Chapter - 8 How Backpropagation Works?
      Chapter - 9 Introduction to CNNs
      Chapter - 10 Architecture of a CNN
      Chapter - 11 Introduction to RNN Model & its application.

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  • Course 5

    Time Series Forecasting

    • Upcoming Batches
    • Curriculum
      Sl. No. Chapter
      Chapter - 1 What is Time Series?
      Chapter - 2 Regression vs Time Series
      Chapter - 3 Trend, Seasonality, Noise and Stationarity
      Chapter - 4 Time Series Operations
      Chapter - 5 Moving Average and Smoothing
      Chapter - 6 Exponentially weighted forecasting model
      Chapter - 7 Correlation and Auto-correlation
      Chapter - 8 Holt's linear trend method
      Chapter - 9 Holt's Winter seasonal method
      Chapter - 10 ARIMA and SARIMA

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  • Course 6

    Natural Language Processing

    • Upcoming Batches
    • Curriculum
      Sl. No. Chapter
      Chapter - 1 Introduction to Natural Language Processing
      Chapter - Understanding Text Analytics
      Chapter - Count Vectorizer
      Chapter - TF-IDF Vectorizer
      Chapter - Stemming
      Chapter - Lemmatization
      Chapter - Understanding Language Structures and Syntax
      Chapter - Natural Language Processing Libraries

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  • Course 7

    Data Base Management System

    • Upcoming Batches
    • Curriculum
      Sl. No. Chapter
      Chapter - 1 Introduction To Data Base Management System
      Chapter - 2 Entity Relationship Model
      Chapter - 3 Relational Model
      Chapter - 4 Keys in Relational Model (Candidate, Super, Primary, Alternate and Foreign)
      Chapter - 5 Normalisation
      Chapter - 6 Transactions and Concurrency Control
      Chapter - 7 Inner Join vs Outer Join
      Chapter - 8 Dimensional Data Modeling
      Chapter - 9 ER Model: Generalization, Specialization and Aggregation
      Chapter - 10 Recursive Relationships

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  • Course 8

    Post Graduate Program In Business Analytics

    • Upcoming Batches
    • Curriculum
      Sl. No. Chapter
      Chapter - 1 Introduction to R
      Chapter - 2 R for Business Analytics
      Chapter - 3 Programming in R
      Chapter - 4 OOPS Concept In R
      Chapter - 5 How To Read The Data In R?
      Chapter - 6 Data Preprocessing
      Chapter - 7 Data Visualization Using R
      Chapter - 8 Introduction to DataBase Management System
      Chapter - 9 Introduction to SQL
      Chapter - 10 NoSQL Databases and Best Practices
      Chapter - 11 Introduction To Machine Learning
      Chapter - 12 Inferential Statistics
      Chapter - 13 Supervised Learning:Linear Regression
      Chapter - 14 Supervised Learning:Classification
      Chapter - 15 UnSupervised Learning:Clustering
      Chapter - 16 Machine Learning for Business Analytics
      Chapter - 17 Tree Based Models
      Chapter - 18 Time Series Forecasting
      Chapter - 19 Operations Research
      Chapter - 20 Data Visualization Using Tableau

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  • Course 9

    Introduction of Python for IEM-UTeMSS

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  • Course 10

    Masters in Data Science Engineering

    • Upcoming Batches
    • Curriculum
      Sl. No. Chapter
      Chapter - 1 Introduction to Data Science
      Chapter - 2 Introduction to programming in Python
      Chapter - 3 Introduction to SQL programming
      Chapter - 4 Working On MY SQL
      Chapter - 5 Exploratory Data Analysis,Data Cleaning,Data Manipulation
      Chapter - 6 Statistical Methods Of Decision Making
      Chapter - 7 Machine Learning-Supervised Learning:Regression
      Chapter - 8 Machine Learning:Supervised Learning:classification
      Chapter - 9 Unsupervised Learning:Clustering
      Chapter - 10 Unsupervised Learning:PCA
      Chapter - 11 Ensemble Techniques:Bagging,Boosting
      Chapter - 12 Data Visualization
      Chapter - 13 Data Science Applications
      Chapter - 14 Time series Forcasting
      Chapter - 15 Text Mining and Sentimental Analysis
      Chapter - 16 Capstone project

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  • Course 11

    Masters in Data Science and Analytics

    • Upcoming Batches
    • Curriculum
      Sl. No. Chapter
      Chapter - 1 Introduction to Analytics:-
      Chapter - 2 Statistical Method for Decision Making:-
      Chapter - 3 Business Finance:-
      Chapter - 4 Marketing and CRM:-
      Chapter - 5 Analytics techniques
      Chapter - 6 Data Mining:-
      Chapter - 7 Predictive Modeling:-
      Chapter - 8 Time Series Forecasting:-
      Chapter - 9 Machine Learning:-
      Chapter - 10 Optimization Techniques:-
      Chapter - 11 Domain Specialization
      Chapter - 12 Visualization and insights
      Chapter - 13 Capstone project

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  • Course 12

    DSF (Data Science Fundamental)

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  • Course 13

    Data Science Professional Certification

    • Upcoming Batches
    • Curriculum
      Sl. No. Chapter
      Chapter - 1 Introduction to programming in Python
      Chapter - 2 Introduction to DataBase Management System
      Chapter - 3 Exploratory Data Analysis
      Chapter - 4 Statistical Methods Of Decision Making
      Chapter - 5 Machine Learning :Regression
      Chapter - 6 Machine Learning: Classification
      Chapter - 7 Unsupervised Learning: Clustering Techniques
      Chapter - 8 Unsupervised Learning:PCA
      Chapter - 9 Ensemble Techniques:Bagging,Boosting
      Chapter - 10 Machine Learning Model Deployment Using Flask
      Chapter - 11 Data Visualization Using Tableau
      Chapter - 12 Data Visualization Using Google Data Studio

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  • Course 14

    DSAP (Data Science Analytics Professionals)

    • Upcoming Batches
    • Curriculum
      Sl. No. Chapter
      Chapter - 1 Introduction to Data Science
      Chapter - 2 Introduction to Business Analytics
      Chapter - 3 Introduction to Python
      Chapter - 4 Working On MySql
      Chapter - 5 Exploratory Data Analysis,Data Cleaning,Data Manipulation
      Chapter - 6 Statistical Method for Decision Making:-
      Chapter - 7 Machine Learning-Supervised Learning,Unsupevised Learning
      Chapter - 8 Ensemble Technique:Bagging,Boosting
      Chapter - 9 Text mining and sentimental Analysis
      Chapter - 10 Time Series Forecasting
      Chapter - 11 Predictive Modeling
      Chapter - 12 Optimization Techniques:-
      Chapter - 13 Marketing and Retail Analytics
      Chapter - 14 Web & Social Media Analytics
      Chapter - 15 Finance & Risk Analytics

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