Comprehensive Data Science – Beginner to Advanced Training

Transform yourself into a Data Scientist by delivering hands-on experience in Maths, Statistics, Machine Learning, Deep Learning and Artificial Intelligence (AI) using Python, R and TensorFlow. The course provides in-depth understanding of Machine Learning and Deep Learning algorithms such as Linear Regression, Logistic Regression, Naive Bayes Classifiers, Decision Tree and Random Forest, Support Vector Machine, Artificial Neural Networks and more.

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Course Overview

  • Introduction to Data Science & Analytics-

    Importance of data science, applications, tools and project methodologies.
  • Maths and Statistics Fundamentals

    Fundamentals of Probability, Linear algebra, Descriptive Statistics, Inferential Statistics,Hypothesis Testing and Exploratory Data Analysis.
  • Machine Learning and Predictive Analytics

    Machine Learning Basics , Data preparation, Correlation and Linear regression,
    Logistic regression, Unsupervised learning , Time series forecasting, Boosting Algorithms, Support Vector Machines (SVM) & kNN  and Text Mining.
  • Deep Learning and Neural networks

    Feed-forward, Recurrent and Gaussian Neural Networks.Understanding Tuning of Neural Networks.


Teaching Hours


Project Hours

Hands on Coding Hours

eLearning Dashboard

Even if you miss a class or a new tool comes in, you can always check in to learn the latest.

Job Assistance Program

Resume building, mock interview, Placement assistance with our industry contacts.

Real world projects

Add impressive enterprise level projects to your portfolio.


Hands on coding

As we strongly believe in learning by practice, we will share with you 24*7 Enterprise level Cloud infrastructure.

Our Students Speak

Beating the Automation Layoffs was a big challenge for me, but, with Data Science training from Factlabs, I got more confident and able to get over it to excel and grow.


Data Scientist, Major IT MNC

Enough of research, comparison and heads up, lets get started and get going!

Introduction to Data Science & Analytics

What is data science/analytics and why is it so important?
Applications of data science/analytics
Different kinds of data science/analytics tools
Data science/Analytics project methodology
Case study

Maths and Statistics

Fundamentals of Math and Probability-linear algebra, Matrics, vectors,Addition and Multiplication of matrics

Descriptive Statistics- Summarizing numeric/categorical/GroupWise data(R & pandas), measures of central tendency and variability, Measures of data variability & distributions, Standard deviation,mean,median,mode, curtosis and skewness

Inferential Statistics-Probability,distributed function and cumulative distributed function,Sampling techniques,Estimators and confidence intervals,Central Limit theorem,Point estimate and Interval estimate,Parametric and non-parametric statistical tests,Analysis of variance (ANOVA),confidence interval for population parameter,Z-distribution and T-Distribution,

Exploratory Data Analysis- Derive initial insights from the data using R and other visualization tools

Hypothesis Testing-Understand how to formulate & test hypotheses to solve various business problems,Type of test and Rejection Region,Type o errors-Type 1 Errors,Type 2 Errors,P value method,Z score Method,T-Test, Analysis of variance(ANOVA) and Analysis of Co variance(ANCOVA),Regression analysis in ANOVA

Data Visualization-Make your data alive with visuals using R and tools like Tableau,Need,Components, Utility and limitations,grammar of graphics,ggplot2, & Seaborn package in R, Python,Visual summary of different data combinations
Practice assignment – Vector manipulations, probability ,5 Point summary BoxPlot, C.L.T

Machine Learning Basics

Converting business problems to data problems
Understanding supervised and unsupervised learning with examples
Reinforcement Learning
Understanding biases associated with any machine learning algorithm
Ways of reducing bias and increasing generalisation capabilites
Drivers of machine learning algorithms
Cost functions
Brief introduction to gradient descent
Importance of model validation
Methods of model validation
Cross validation & average error

Data preparation

Introduction to NumPy arrays, functions & properties
Introduction to Pandas & data frames
Importing and exporting external data in Python
Data Handling in Python using NumPy & Pandas
Needs & methods of data preparation
Handling missing values
Outlier treatment
Transforming variables
Derived variables
Binning data
Modifying data
Data processing with dplyr package
Using SQL in R
Feature engineering using Python
Practice assignment

Correlation and Linear regression
Simple linear regression
Regularisation of Generalised Linear Models
Multiple linear regression
Ridge and Lasso Regression
Model diagnostics and validation
Understanding Covariance and Colinearity
Understanding Heteroscedasticity
Case Study
Logistic regression

Moving from linear to logistic regression
Model assumptions and Odds ratio
Model assessment and gains table
Cost function for logistic regression
Application of logistic regression to multi-class classification.
Methods of threshold determination and performance measures for classification score models
Confusion Matrix, Odd’s Ratio,ROC curve and KS statistic
Case study

Unsupervised learning

Need for clustering/segmentation
Criterion of clustering/segmentation
Types of distances
Clustering algorithms
Hierarchical clustering
K-means clustering
DBSCAN Clustering
Deciding number of clusters
Need for dimensionality reduction
Principal Component Analysis (PCA)
Independent components analysis(ICA)
Difference between PCAs and Latent Factors
Recommender System-collaborative filtering algorithm
Factor Analysis

Association Rule Mining
Case study

Time series forecasting

What are time-series?
Need for forecasting
Trends, seasons, cycles
Exponential smoothing-Holt Winters method
Case Study

Decision trees

Gini impurity index
Decison trees algorithms
Understanding Kart Model
Classification Rules- Overfitting Problem
Stopping Criteria And Pruning
How to Find final size of Trees
Regression trees
Tuning tree size with cross validation
Introduction to bagging algorithm
Random Forests
Grid search and randomized grid search
ExtraTrees (Extremely Randomised Trees)
Partial dependence plots

Support Vector Machines (SVM) & kNN

Introduction to idea of observation based learning
Distances and similarities
k Nearest Neighbours (kNN) for classification
Brief mathematical background on SVM
Regression with kNN & SVM
Case Study

Text Mining

Gathering text data using web scraping with urllib
Processing raw web data with BeautifulSoup
Interacting with Google search using urllib with custom user agent
Collecting twitter data with Twitter API
Naive Bayes Algorithm
Feature Engineering with text data
Sentiment analysis
Case study

Neural networks

Master Feed-forward, Recurrent and Gaussian Neural Networks. This is your way into AI!
Understanding Tuning of Neural Network

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