Become a Data Scientist

# Data Science course training program

Data rules the world and the amount of data being disposed of today is beyond our imagination. This eruption in the modern world creates several challenges every day, affecting businesses and enterprises in different ways. This is where companies rely on Data Science for managing, analyzing the accurate data, and using it appropriately.

Apart from finding the data, analyzing it, and using it accurately according to the business, it is the greatest challenge every organization is experiencing right now. Companies need the right talents for solving complex problems, analyzing the correct data, and managing it on various platforms.

## What is the scope of a data scientist in India?

Data Science is the most trending job right now, and the average salary of a Data Scientist would be Rs. 708,012/- in India. Today Data Science is being used across all the platforms as healthcare, finance, education, banking, and retail. Data eruption never stops, and businesses need the people with the ability to interpret the data to extract insights and help them in decision making, making a better product, managing business efficiently, effective marketing, forecasting and analyzing the business process, and creating compelling strategies and leveraging the company.

With each passing year, this evolution of data continues to increase into massive piles, and it is not possible with the traditional methods to derive the data, analyze it, and make decisions out of it. Businesses demand a more advanced skill set for processing and analyzing it. This is where data science helps to make this process easier.

## Why Technology for All for Data Science Course Training?

We are incepted on the mission of bringing technology to life. To make everyone access the technology, make use of it, and achieve the dream jobs.

This course provides full-fledged knowledge about data science, and with real-time projects, an individual will encounter the challenges faced in the real world. Through case studies, individuals will learn the roles and responsibilities of a data scientist such as data mining, data wrangling, data exploration, Big data processing, data visualization, and more.

Our Data Science course in Hyderabad will also help in seeking the highest paid job as we assist individuals for career advancement and transformation. We carefully curate the course curriculum to ensure that the individual is taught the advanced concepts of data science. This helps them in solving any challenge that occurs. Along with that, we also make students work on real case studies and use-cases derived.

## New Batch details:

Starts on: **2nd August 2021 @ 6PM**

Duration: **3 months**

Fee: **Rs.14999/-**

Early bird offer: **Rs.8999/-** (before **24th July** )

## Who can learn this Data Science Training Course?

This Data Science Course Training in Hyderabad in the best suitable for

- The data science career enthusiasts who wish to
**advance their knowledge**. - Ideal for
**graduates**who want to begin their career. **Any working professional**who wants to transform the career.- Ideal for
**Managers, Business analysts, Database Administrators, Networking Operators, IT Developers & Software Professionals**.

This course covers the entire concepts where both job seekers & working professionals can shift their careers and excel in the existing technologies. Right from the basic concepts such as Python, R to advanced concepts such as Machine Learning, AI, Business Analytics, Predictive Analytics, Text Analytics, and Text Analytics are covered in this course.

### Data Science Training Curriculum

### Module 1: Python for Data Science

1. Introduction

a. Download and install anaconda

b. Overview of jupyter notebook

2. Python for AI/ML

a. Why Python?

b. Application areas

c. HelloWorld in Python

d. Python vs other languages

e. Indentation

f. Keywords

g. Python Operators

3. Data Types and Data Structures

a. Identifiers

b. int, float, complex, bool

c. Strings

d. List, Tuple, Dictionary, Set

4. Control Flow and Decision Statements

a. if, elif and else statements

b. Nested conditional statements

c. for loops

d. while loops

e. for else

f. while else

g. List Comprehension

5. Functions and Modules

a. Introduction

b. def keyword

c. Scope of variables in function

d. Defining and Calling a function

e. Lambda, Map, Reduce and Filter

f. Introduction to Modules and Packages

g. import keyword

h. Creating a Package in Python

i. Understanding _name_variable

### Module 2: Version Control (Git) and Flask Framework

1. Version Control (Git)

a. Introduction to Version Control

b. Introduction to Git and GitHub

c. Why Git

d. Centralized and Distributed Version Control System

e. Installing Git

f. Git Basics

g. Forking

h. Cloning

i. Making changes to Local Repositories

j. Committing

k. Branching

l. Collaborating with Multiple Developers

2. Web Application Development

a. Introduction to Client Server

b. Introduction to Flask Web Framework

c. Basics of Routing

d. Dynamic Routing

e. Introduction to HTML

f. HTML Forms

g. Templates

h. Jinja Templating

i. Template Inheritance

j. Flask WTForms

3. Building Flask Application

4. Cloud Deployment

a. Introduction to Cloud

b. Heroku Deployment

c. AWS Deployment

5. Introduction to Containerisation and Docker

### Module 3: Data Analysis using Python

1. Statistics and Probability

a. Fundamentals – What is Descriptive Statistics?

b. Mean, median and mode

c. Range, IQR, variance and standard deviation

d. Covariance and Correlation

e. Correlation

f. Normal Distribution

g. Fundamentals – Random Experiment, Event, Axioms, etc

h. Conditional Probability

i. Random Variable

j. Gaussian Distribution

2. Data Analysis with Numpy and Pandas

a. Intro to Numpy

b. Creating an array

c. Indexing and Slicing

d. Statistical Operations using Numpy

e. Introduction to Pandas

f. Introduction to Series and Dataframe

g. Working with .csv

h. Working with .xlsx

3. Data Manipulation with Pandas

a. Groupby, Pivot tables and Crosstabs

b. Re-indexing

c. Handling missing Values

d. Outlier treatment

e. Duplicates

f. Visualization of basic plots using Pandas

4. Data Visualization with Matplotlib and plotly

a. Histogram

b. PDF

c. Scatter Plot

d. Pair plot

e. Strip Plot

f. Box Plot

g. Violin Plot

h. Count Plot

5. Exploratory Data Analysis – Case Studies (Mini Projects)

a. Univariate analysis

b. Bivariate Analysis

c. Python Implementation on various datasets

### Module 4: End to End Web Scraping

1. Regular Expressions

a. Understanding unstructured data

b. Meta Characters

c. Literals

d. Regex in Python

e. import re

f. Pattern Matching

2. Introduction to Web Scraping

a. requests module

b. Installing bs4 and BeautifulSoup

c. Loading the web pages using requests

d. Extracting the HTML from Web Pages

e. find() and find_all()

3. Project on Web Scraping

a. Data Mining

b. Data Preprocessing

c. Data Visualization

### Module 5: Machine Learning

1. Moving to Machine Learning

a. Why learn AI/ML?

b. AI vs ML vs DL

c. Applications

d. Supervised vs Unsupervised Learning

e. Classification vs Regression

2. Linear Algebra

a. Introduction and why linear algebra?

b. Fundamental of Vectors

c. Fundamental of Matrices

d. Vector Algebra

e. Dot Product

f. Euclidean Distance

g. Manhattan Distance

h. Projection

3. Linear Regression

a. Introduction

b. Equation of hyperplane

c. Mathematical Formulation and intuition

d. OLS Assumptions

e. Hyperparameter Tuning

f. Residual Analysis

g. Polynomial Regression

h. Non-linear transformation

i. Feature Selection – Forward and Backward

j. Case Study (Mini Project)

4. K-Nearest Neighbors

a. Introduction

b. Intuition

c. Lazy Learner

d. Deciding the number of neighbors

e. Improving KNN performance

f. Case Study (Mini Project)

5. Logistic Regression

a. Geometric Intuition

b. Regression vs Classification

c. Linear Regression vs Logistic Regression

d. Mathematical Formulation

e. Sigmoid Function

f. Understanding the Decision Boundary

g. Binary vs Multiclass classification

h. Case Study (Mini Project)

6. Performance Measurement of Models

a. Accuracy

b. Confusion Matrix

c. Precision and Recall

d. F1 Score

e. ROC AUC

f. Log Loss

g. R square

h. Case Study (Mini Project)

7. Support Vector Machines

a. Geometric Intuition

b. Hard and Soft Margin Classification

c. Kernel Trick

d. RBF-Kernel

e. Tuning Hyperparameter

f. Case Study (Mini Project)

8. Decision Trees

a. Introduction (Rule Based Learning)

b. How to build a decision tree

c. Classification and Regression Trees (CART)

d. Entropy

e. Gini Impurity

f. Overfitting and Pruning

g. Tuning Hyperparameters

h. Case Study (Mini Project)

9. Model Tuning and Cross Validation

a. Introduction to Cross Validation

b. k-fold Cross Validation

c. Bias-Variance Trade-off

d. Hyperparameter Tuning

10. Ensemble Methods – Bagging and Boosting

a. What is an Ensemble

b. Random Forest Algorithm

c. Ada Boosting

d. Gradient Boosting

e. Tuning hyperparameter

f. Case Study (Mini Project)

11. Clustering – Unsupervised Learning

a. What is clustering?

b. Application

c. K – Means Algorithm

d. Centroids

e. Elbow Method for deciding ‘K’ in K-Means

f. Code Sample

g. Python Implementation

h. Hierarchical Clustering

i. Agglomerative Clustering

k. Practical issues with Clustering Algorithms

l. Case Study (Mini Project)

12. Principal Component Analysis

a. Introduction to Dimensionality Reduction

b. Principal Components

c. EigenValues and EigenVectors

d. Transformation of Data

e. Proportion of variance explained

f. Case Study (Mini Project)

13. Glance at state of the art Deep Learning Supervised and Unsupervised Algorithms

14. Machine Learning Application Development and Deployment

15. End to End Project implementation in Machine Learning

a. Regression

b. Classification

c. Clustering

d. Recommendation Engines

### Module 6: Deep Learning (Computer Vision and NLP)

1. Introduction to Neural Networks

a. Introduction to Neurons and Perceptrons

b. Sigmoid Neuron

c. Activation Function

d. Cost Functions

e. Gradient Descent and Stochastic Gradient Descent

f. Feedforward and Backpropagation

2. Deep Learning Frameworks

a. Installing Tensorflow

b. Tensorflow and Keras

c. Basic syntax

d. Saving Models

e. Tensorboard

3. Artificial Neural Network

a. Intuition behind Back Propagation

b. Computing the derivatives

c. Training Deep Neural Networks

d. Optimization Algorithms

e. Activation functions and Initialization Methods

f. Sigmoid, Tanh and ReLu

g. Regularization Methods

h. Overfitting and Regularisation

i. Early Stopping

4. Computer Vision – OpenCV

a. Introduction to Vision Tasks

b. Introduction to OpenCV

c. Working with Images

d. Filtering

e. Preprocessing an image with OpenCV

f. Reshaping and Resizing

g. Gaussian Blur, Dilation and Erosion

h. Contours, Hull and Blobs

i. Working with Videos

j. Hands-on Demo

k. Mini Project

5. Convolution Neural Network

a. Introduction to CNN

b. Convolution Operation

c. Stride

d. Padding

e. Max Pooling

f. VGG16

g. Transfer Learning

h. AlexNet

i. GoogleNet

j. ResNet

k. Implementing CNN and Transfer Learning in Keras

6. Industry Use Case of CNN’s

a. Semantic Segmentation – U-Net

b. Object Detection – YOLO and SSD

c. RCNN – Fast and Faster

d. Siamese Network as metric learning

e. Hands-on demo

7. Natural Language Processing

a. Tokenization

b. Stop Words

c. Special Characters

d. Regular Expressions

e. Stemming

f. Lemmatization

g. Bog of Word

h. TF-IDF

i. Word2vec

j. Glove

k. POS Tagger

l. Named Entity Recognition

m. Code Sample

n. Sentiment analysis project

8. Recurrent Neural Network

a. Introduction to RNN

b. Architecture

c. Types of RNN

d. Training RNNs

e. Bidirectional RNN

f. LSMTs

g. GRU

h. Implementation in Keras

9. Attention Models

a. Introduction to Encoder Decoder

b. Auto Encoder

c. Introduction to BERT

d. Intuition and Application of GAN’s

e. Industry applications of GPT-1, 2 and 3

10. Deep Learning Project Implementations

### Module 7: Data Analysis with SQL and Tableau

1. SQL for Data Science

a. Introduction to Databases

b. Basics of SQL – Select, from and where

c. DML, DDL and DCL

d. Limit, offset, orderby, distinct, logical operators

e. Joins

f. SQL Aggregation – count, min, max, avg, sum

g. Insert, update, delete

h. Create, alter, add, modify, drop, truncate

i. Grant, revoke

j. Data manipulation and analysis using SQL

2. Tableau for Data Science

a. Install Tableau for Desktop

b. Connect Tableau to a dataset

c. Analyze, Blend, Join and Calculate Data

d. Tableau for Visualization

e. Various Charts, Plots and Maps

f. Data Hierarchies

g. Calculated Fields

h. Filters

i. Creating Interactive Dashboards

j. Adding actions to Dashboards

## What are the job roles associated with the Data Science Career?

As data science is all about mining the data in this tech world, data scientists play a crucial role in extracting the information and processing it further. There are job roles as

- Machine learning Engineer
- Business Intelligence Analyst
- Data Warehouse Architect
- Business Analyst
- Statistician
- Systems Analyst

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#### Data Science Course FAQ's

##### Will I get any placement assistance after completing the course?

We will provide complete guidance such as mock-up interviews, finding the right jobs after completing the course

##### Will there be any backup classes provided if I miss the course?

Yes, there will be backup classes. There will be complete lifetime access to the LMS if you miss any crucial concepts during the training.

##### What is the certification provided after the training?

Once, after completing the Data Science Course Training in Hyderabad, individuals will be provided with the certification from IBM based on the course’s level.

#### Can I work on any real-time projects?

As a part of the training, our students would get a chance to work on real-time projects and business use cases that help them to build the challenges and solve the complexities in Data Science.

## What are the job roles I can apply for after the course?

You can apply for the highly paid roles as analyst roles, Data Engineer/ Architect, Machine Learning Engineer, Big Data Engineer.