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One of India’s Top Rated Training Centres for Python programming,
Data Science, AI & Web development- Python Training institute from
the house of TGC India has trained thousand of students in the span
of last 25 years.

What Makes You Master The Art ?

Learn From Experts

At TGC, We have expert trainers sharing their rich experience in Data Science Process

Assignments

The entire training session is based on theories, practical projects & assignments. We also emphasize bringing many industry guest trainers.

Practical Training

At TGC, We boast about our sate-of-the-art labs equipped with all necessary hardware & software.

COURSE STRUCTURE

Part 1 - Python

Introduction to Python

  • Introductory Remarks about Python
  • A Brief History of Python
  • How python is differ from other languages
  • Python Versions
  • Installing Python and Environment Setup
  • IDLE
  • How to execute Python program
  • Writing your first Python program

Variables, Data Types & Operators

  • Memory mapping of variables
  • Keywords in Python
  • Comments in python
  • Arithmetic Operators
  • Assignment Operators
  • Comparison Operators
  • Logical Operators
  • Membership Operators

Data types in Python

  • Numbers
  • Strings
  • Lists
  • Tuples
  • Dictionary
  • Sets
  • 3D asset Creation

Numbers and Strings

  • Intro. to Python ‘Number’ & ‘string’ data types
  • Properties of a string
  • String built-in functions
  • Programming with strings
  • String formatting

Lists and Tuples

  • Intro to Python ‘list’ data type
  • Properties of a list
  • List built-in functions
  • Programming with lists
  • List comprehensio

Dictionary and Sets

  • Intro to Python ‘dictionary’ data type
  • Creating a dictionary
  • Dictionary built-in functions
  • Set and set properties
  • Set built-in functions

Decision making & Loops

  • Intro of Decision Making
  • Control Flow and Syntax
  • The if Statement
  • The if...else Statement
  • The if...elif...else Statement
  • Nested if...else Statement
  • The while Loop
  • break and continue Statement

User defined Functions

  • Intro of functions
  • Function definition and return
  • Function call and reuse
  • Function parameters
  • Function recipe and docstring
  • Scope of variables
  • Recursive functions
  • Lambda / Anonymous Functions

Modules and Packages

  • Module
  • Importing module
  • Standard Module - sys
  • Standard Module - OS
  • The dir Function
  • Packages
  • Exercise

Exception Handling in Python

  • Understanding exceptions
  • Run Time Errors
  • Handling I/O Exceptions
  • try, except, else and finally statement
  • raising exceptions with: raise, assert

File Handling in Python

  • Working with files
  • File objects and Modes of file operations
  • Reading, writing and use of ‘with’ keyword
  • read(), readline(), readlines(), seek(), tell()
  • Handling comma separated value files

Regular expression

  • Pattern matching
  • Meta characters for making patterns
  • re flags
  • Use of match() , sub() , findall(), search(), split()

Part 2 - Advanced Excel

Intro to MS Excel Environment

  • Excel Interface
  • Features of MS Excel
  • MS Excel functions
  • Understanding of data calculations in Excel
  • Understanding of Data Tools Panel
  • Excel Different Types of Charts Creation

DateTime in Python

  • Dashboards
  • Designing Sample Dashboard
  • Step-by-step Excel dashboard tutorial
  • Representation of Dashboard data
  • Organizing data in Dashboard
  • Tips and Tricks to enhance dashboard designing

Project Work

  • Dashboard Analysis Project in Excel
  • Stock Management System Project in Excel

Part -3 SQL

Overview of SQL

  • SQL
  • Database Concepts
  • What is Database Package
  • Understanding Data Storage
  • Understanding of Data Tools Panel
  • Relational Database (RDBMS) Concept

Part-1 SQL

  • SQL Basic
  • SQL Commands
  • DDL, TCL, DML & DQL
  • DDL: create, alter, drop, rename
  • DML: insert, update, delete
  • DQL: select

Part-2 SQL

  • TCL: rollback, commit
  • SQL where
  • SQL operators
  • SQL like, not like
  • SQL between, not between
  • SQL order by

Part 4- Data Analytics

PYTHON LIBRARIES

  • What is data analysis?
  • Why python for data analysis?
  • Python Libraries Installation and setup
  • Ipython
  • Jupyter Notebook
  • Relational Database (RDBMS) Concept

NUMPY ARRAYS

  • Numpy Arrays
  • Numpy Data types
  • Numpy Array Indexing
  • Numpy Mathematical Operations
  • Indexing and slicing
  • Manipulating array shapes

WORKING WITH PANDAS

  • Data structure of pandas
  • Pandas Series
  • Pandas dataframes
  • Data aggregation with Pandas
  • DataFrames Joining
  • DataFrames Handling missing data

Part -5 Data Visualization

Matplotlib

  • Basic matplotlib plots
  • Line Plots
  • Bar Plots
  • Pie Plots
  • Scatter plots
  • Histogram Plots

Seaborn

  • Categorical Plots
  • Bar Plots
  • Box Plots
  • Heatmaps Plots
  • Pair Plots
  • Regression Plots

Plotly – Python Plotting

  • Plotly
  • Geographical Plotting
  • Choropleth Maps – Part 1
  • Choropleth Maps – Part 2
  • Choropleth Exercises
  • Projects using Analysis and Visualisation

Part 6- Statistics, Probability & Business Analytics

Basic Statistics

  • Data types and their measures
  • Measures of Central Tendency
  • Arithmetic mean
  • Harmonic mean
  • Geometric mean
  • Median

Probability Distributions

  • Intro of probability
  • Conditional probability
  • Normal distribution
  • Uniform distribution
  • Frequency distribution
  • Central limit theorem

Hypothesis Testing

  • Concept of Hypothesis Testing
  • Statistical Methods
  • Z-Test
  • T-Test
  • One Way Anova Test
  • Two Way Anova Test

Part 7- Machine Learning

Machine Learning

  • What is Machine learning?
  • Overview about scikit-learn package
  • Types of ML
  • Basic steps of ML
  • ML algorithms
  • Machine learning examples

Data Preprocessing / Data Cleaning

  • Dealing with missing data
  • Identifying missing values
  • Imputing missing values
  • Drop samples with missing values
  • Handling with categorical data
  • Nominal and Ordinal features

KNN Classifiers

  • K-Nearest Neighbours (KNN)
  • KNN Theory
  • KNN implementation
  • KNN Project Overview and Project Solutions

Regression Based Learning

  • Linear Regression Theory
  • Dependent and independent Variables
  • Linear Regression with Python implementation
  • Multiple linear regression
  • Polynomial regression
  • Regularization

Logistic Regression for Classification

  • Logistic Regression Theory
  • Binary and multiclass classification
  • Implementing titanic dataset
  • Implementing iris dataset
  • Sigmoid and softmax functions
  • Nominal and Ordinal features

Decision Tree Classification

  • Entropy and Information gain
  • Intro to bagging algorithm
  • Implementation with iris dataset
  • Visualizing Decision Tree
  • Ensemble Learning
  • Random forest

Support Vector Machines

  • Introduction to SVM
  • Working of SVM and its uses
  • Working with High Dimensional Data
  • Kernel, gamma, margin
  • Breast Cancer Prediction Project using SVM

Naive Bayes Algorithm

  • Conditional Probability
  • Overview of Naïve Bayes Algorithm
  • Feature extraction
  • CountVectorizer
  • TfidfVectorizer
  • Nominal and Ordinal features

Clustering Based Learning

  • K-means Clustering Algorithm
  • Elbow technique
  • Silhouette coefficient
  • K Means Clustering Project Overview
  • K Means Clustering Project Solutions

Recommendation System

  • Content based technique
  • Collaborative filtering technique
  • Evaluating similarity based on correlation
  • Classification-based recommendations

Natural Language Processing

  • Install nltk
  • Tokenize words
  • Tokenizing sentences
  • Stop words with NLTK
  • Nominal and Ordinal features

Working with OpenCV

  • Basic of Computer Vision & OpenCV
  • Reading and writing images
  • Resizing image
  • Applying image filters
  • Writing text on images

Part 8- Deep Learning

Deep Learning

  • What is Deep Learning?
  • Deep Learning Packages
  • Deep Learning Applications
  • Building deep learning environment
  • Installing tensor flow locally
  • Understanding Google Colab

Tensor Flow Basics

  • What is tensorflow?
  • Tensorflow 1.x v/s tensorflow 2.x
  • Variables, Constants, Placeholder
  • Scalar, vector, matrix
  • Operations using tensorflow
  • Tensor Flow Computational graph

Artificial Neural Network

  • What is Artificial Neural Network
  • How neural network works?
  • Perceptron
  • Multilayer Perceptron
  • Feedforward
  • Back propagation

Activation Functions

  • Linear Activation Function
  • Sigmoid function
  • Hyperbolic tangent function
  • ReLU –rectified linear unit

Optimizers

  • What does optimizers do?
  • Gradient descent
  • Stochastic gradient descent
  • Learning rate , epoch

Building Artificial Neural Network

  • Using scikit implementation
  • Using tensorflow
  • Understanding mnist dataset
  • Initializing weights and biases

Deep Learning Optimizer

  • SGD with momentum
  • RMSprop
  • AdaGrad
  • Adam

Deep Neural Network Using Keras

  • What is keras?
  • Keras fundamental for deep learning
  • Keras sequential model and functional api
  • Saving and loading a keras model

Convolutional Neural Network

  • CNN architecture
  • Convolutional operations
  • Pooling, stride and padding operations
  • Data augmentation

Part 9- R Language

Introduction to R

  • What is R
  • History of R
  • Features of R
  • Obtaining and managing R
  • Installing R
  • Packages

Data Types and Objects

  • Data Types
  • Variables in R
  • Scalars
  • Vectors
  • Factors
  • Numbers

Data Management

  • Reading Data
  • Writing data
  • Reading data files with tables
  • Files connection
  • Reading lines of Text files
  • Sorting Data

Import and Export Data in R

  • Importing data in to R
  • CSV File
  • Excel File
  • Import data from text table

Control Structures and Functions

  • Control statements [if,if..else,next,return]
  • Loop statements [while, for, repeat]
  • Functions
  • Function arguments & options

Database connectivity with R

  • How to install RMysql Package
  • How to connect R to Mysql Database
  • Operation on Mysql Queries on R

Part 10- Data Analytics using R

R LIBRARIES

  • What is data analysis?
  • Why R for data analysis?
  • Essential R Libraries Installation and setup
  • Rstudio Overview

MANAGING DATA FRAMES WITH THE DPLYR PACKAGE

  • The dplyr Package
  • Installing the dplyr package
  • select()
  • filter()

DATA CLEANING WITH THE TIDYR PACKAGE

  • The tidyr Package
  • Installing the tidyr package
  • Installing the data.table package
  • Cleaning data

Part11- Data Visualization using R

GRAPHICS AND PLOTTING USING R Basic Plotting

  • Basic plots in R
  • Line Plots
  • Bar Plots
  • Pie Plots
  • Box Plots
  • Scatter plots

ADVANCE PLOTTING GGPLOT 2 Visualization

  • Layers of ggplot2 package
  • How to install ggplot2 package
  • Histogram Plots
  • Scatter plots
  • Bar Plots
  • Box Plots

Interactive Visualizations with Plotly

  • Overview of Plotly and Interactive Visualizations
  • How to install Plotly Interactive Visualization Package
  • Plotly Visualizations
  • Cleaning data

Part 12- Core Tableau for Data Analysis

Tableau Desktop part-1

  • Introduction to Tableau
  • What is Tableau?
  • Kinds of tableau
  • Tableau architecture
  • Overview to different versions

Tableau Desktop part-2

  • Installation of Tableau Desktop
  • Understanding tableau user interface
  • Connect Tableau to Datasets
  • Data analysis with tableau
  • Bar Charts

Tableau Desktop part-3

  • Scatter Charts
  • Pie Charts
  • Creating maps and setting map options
  • Creating Dashboards
  • Interactive Dashboards
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Projects Covered

NO ONE WILL TEACH YOU THIS MUCH...

  • Web Scraping — Food Reviews Site
  • Web Scraping — Online Course Site
  • Identifying the risk factors of heart disease
  • Building a Covid-19 Dashboard
  • Sentiment Analysis on Food Reviews
  • Life Expectancy Prediction
  • Breast Cancer Analysis
  • Cricket score Prediction
  • Fake News Detection:
  • Detection of Red light jumping using Computer vision
  • Industry Feedback Sessions
  • Internship In TGC Studios
  • Final Portfolio
  • Mock Interviews
  • Final Placement
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I enrolled for Python and very much satisfied with the training program, I will give Python Training Institute the highest number. My first thank goes to my SIR whom I’m pressed the most. My best wishes for the center.
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Amazing classes, great staff and wonderful lectures, all who are interested in learning python should have no confusion but just come here.”
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I had a very constructive educational experience at tgc. The facility is very humble and knowledgeable. They not only develop your technical ability but also sharpen your creative skills. You can have high expectations from this place.
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Python Course at Python Training (TGC) south ex. was completed on time. I am fully satisfied with the quality of education provided by our teacher Uttam Saxena and the infrastructure provided by the institute.
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I was not sure what to expect because the class was taught remotely, but I was quite impressed with the entire process. The facility was very professional and there were no problems with connectivity. Trainer was an expert and was very clear in his method of teaching. Go for tgc india for graphic design course in delhi.
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Overall I loved all the classes I have attended in tgc under the guidance of parul mam she explain very well and she shared her experience which helps all of us. I will recommend others to join tgc.
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My experience with this institute and their staff was amazing. I loved the ambiance here and the way teachers deal with students was quite satisfactory. I personally liked Ashwani sir’s way of teaching, he is an amazing competent teacher, frank in nature and diligent too. I would recommend this institute to anyone looking for web design course
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Faculty is excellent and environment also great. Faculty is Supportive to students and always ready to resolve your problems. I m suggested to you please take demo of web design atleast for knowing your faculty. Ashwani sir is great in teaching and their way of teaching is excellent.
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I had one of the best experiences with TGC India, Team guided me properly. Trainers were friendly. Thank you so much all of you! It was a great experience.
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Data Science College in Delhi -FAQs

The course of data science is independent regardless of the background. All you need is to have an interest in data, statistics, and management of the same.

TGC has a state-of-the-art infrastructure and an experienced faculty team of Data Sciencs, Python, compositors, scriptwriters, typographers, graphic designers, VFX artists, and cameramen to teach you the best. The hygienic surroundings are provided. Students will gain confidence to interact with colleagues and friends. Classes are being provided in small cohesive batch sizes to interact with the trainer team on a regular basis.

The data science course comes with knowledge for everyone. Even the beginners who do not know to code can learn it in the fundamentals. Once you know, you are all set to rock!

Yes, of course! Data scientists grow through various stages, just like others. They grow from Data scientists to senior and then to chiefs. They can also become a data science leader and further set up a venture.

Knowledge of data science is a quality that firms look around in the candidates today. Having a certification will help you add value to your resume, enhance knowledge, and outshine others.

Statistics is the core to realizing the best in data science. You only need to know the basics like mean, median, mode, forecasting, hypothesis, etc. All you need is to have the interest!

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Opp. South Ext. Metro Station Gate No. 2 (Pink Line)
New Delhi-110049 (India)

Tel: +91-9582786407 
Email: [email protected]

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4, Park End, 2nd Floor, Vikas Marg (Above Prince jeweller’s) Preet Vihar

Opposite to Metro Pillar No. 100, Delhi-110092

Tel: +91 9582786407
Email: [email protected]

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