Data Science Training

Data Science Training

This Data Science training will introduce you to Data Science concepts, Machine Learning and Deep Learning concepts. You will also learn the tools used in Data Science, Importance of AI and what all opportunities are there after this  Data Science Course.

During this course you will learn Statistics and Python as well which are cery much needed to understand this Data Science training. Later during this course you will get introduce to Machine Learning in details. 

You will also able to do Three end-to-end Machine Learning projects so that you understand the Machine Learning concepts clearly. 

This Data Science course will also introduce you to Deep Learning Concepts like Artificial Neural Networks, Convolutional Neural Networks and Recurrent Neural Networks. You will also able to do Three end-to-end Deep Learning implementation projects so that you understand the Deep Learning concepts in depth. 

Data Science is a rewarding career that allows you to solve some of the world's most interesting problems!

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Course Information

Data Science Course Duration:  60 Hours

Data Science Training Timings:  Week days 1-2 Hours per day (or) Weekends: 2-3 Hours per day

Data Science Training Method:  Online/Classroom Training

Data Science Study Material:  Soft Copy

Course Content

Data Science Learning and Implementation Program

Introduction to Data Science:

  • What is Data Science
  • Roll of Machine Learning and Deep Learning
  • Use case of Data Science
  • Tools used in Data Science
  • Lifecycle of Data Science
  • Importance of AI
  • Opportunities in Data Science


  • Inferential vs Descriptive Statistics
  • Variable Measurements
  • Central Tendency Measures
  • Mean, Mode and Median
  • The Story of Average
  • Dispersion Measures
  • Range, Variance and Standard Deviation
  • Five Number Summary
  • Data Distributions
  • Central Limit Theorem
  • Sampling Methods
  • Hypothesis testing
  • Confidence Level
  • Degrees of freedom
  • Correlation vs Regression


  • Python Download and Installation
  • Basic Syntax
  • Variables
  • Operators
  • Numbers and Strings
  • Lists, Tuples and Arrays
  • Loops
  • Functions
  • Pandas
  • Numpy
  • Handling packages
  • Data Visualization

Machine Learning:

1. Data Preparation

  • Load Data
  • Univariate Analysis
  • Multivariate Analysis
  • Outlier Deduction
  • Z Score
  • Inter Quartile Range
  • Data Scaling
  • Algorithm Evaluation
  • Evaluation Metrics
  • Baseline Models

2. Regression Algorithms

  • Simple Linear Regression
  • Multivariate Linear Regression
  • Logistic Regression
  • Confusion Matrix
  • Perceptron

3. Nonlinear Algorithms

  • Classification Trees
  • Regression Trees
  • Naive Bayes
  • k-Nearest Neighbours
  • Support Vector Machines

4. Ensemble Algorithms

  • Bagging
  • Random Forest
  • Boosting and AdaBoost

5. Unsupervised Learning

  • K means Clustering

6. Natural Language processing

  • NLKT
  • Bag of Words
  • Sentiment Analysis

7. Time series forecasting

  • Auto regression
  • Moving Averages
  • Autocorrelation

Three end-to-end Machine Learning projects as below:

1. Define Problem

2. Load Data from Different Data Sources

3. Analyze Data

  • Understand Data With Descriptive Statistics
  • Understand Data With Visualization

4. Prepare Data

  • Pre-Process Data
  • Feature Selection

5. Evaluate Algorithms

  • Resampling Methods
  • Algorithm Evaluation Metrics
  • Compare Machine Learning Algorithms
  • Model Selection

6. Algorithm Parameter Tuning (Improve Results)

7. Model Finalization (Present Results)


Deep Learning:

1. Artificial Neural Networks

  • Neurons
  • Networks of Neurons
  • Training Networks
  • Tensorflow
  • Keras
  • Build ANN with Tensorflow and Keras

2. Convolutional Neural Networks

  • Convolutional Layers
  • Pooling Layers
  • Fully Connected Layers
  • Convolutional Neural Networks Best Practices

3. Recurrent Neural Networks

  • Long Short-Term Memory Networks
  • LSTM Network For Regression
  • LSTM Network For Classification


Three end-to-end Deep Learning Implementations:

1. Deep Learning for Natural Language Processing

  • Bag-of-Words
  • Word Embeddings
  • Text Classification
  • Sequences

2. Deep Learning for Time Series Forecasting

  • Prepare Time Series Data for CNNs and LSTMs
  • MLPs for Time Series Forecasting
  • CNNs for Time Series Forecasting
  • LSTMs for Time Series Forecasting

3. Deep Learning for Computer Vision

  • Image Data Preparation
  • Convolutions and Pooling
  • Convolutional Neural Networks
  • Image Classification
  • Object Detection
  • Face Recognition

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