Advance Data Science Course | 100% Placement Assistance

Bugspotter's Industry Oriented Advance Data Science Course

100% Placement Assistance | Offline + Live Online Sessions

4 Month Personalized Live Python Data Science Training is taught by industry experts in a comprehensive & question-oriented format.

Enroll Before: December 07, 2024

Bugspotter's Industry Oriented Advance Data Science Course

100% Placement Assistance | Offline + Live Online Sessions

4 Month Personalized Live Python Data Science Training is taught by industry experts in a comprehensive & question-oriented format.

Enroll Before: December 07, 2024

Key Highlights Of The Advance Data Science Course

Get familiar with our online Python Data Science course syllabus.

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Syllabus

Our hybrid online Advance Data Science Course is designed to teach students the basics to the advanced level concepts of Python Data Science with practice assignments and offline in-class projects which helps them to get placed in MNC’s.

Term 1

In this term, you will learn how to ace Python Basics, Python OOPS and Python Libraries like Pandas, Matplotlib, Numpy, etc…

Module 1 :- Python

Python Basics :

Why python
Python IDE
Basics of programming
Variables , Data Types
Conditional statements
Loops
Logical Thinking
Data Structures
Functions and types of arguments
Lambda Functions
memory Management
garbage collector
Copies - shallow copy, deep copy
Higher Order Functions - Map , Reduce , Filter
Iterable , Iterator , generator
Exception handling
Programming interview questions

Python OOPS :

Class
constructor and its types , Destructor
Types of variables - instance , static
Inheritance - Single , Multiple , Multilevel , Hierarchical
polymorphism
duck typing
Overloading - method , Operator , constructor
overriding - method , Constructor
Super Function
Encapsulation
access Modifiers
Abstraction
monkey patching

Module 2:- Python Libraries

Pandas :

Introduction to Pandas
Series Data Structure
Data Frame Data Structure
Merging DataFrame
Read Complex CSV , JSON , excel Files using pandas
Write to File
Data Frame Manipulation - head , Tail , Describe , shape ,Drop , inplace
loc & I=iloc
Apply Function
Value count
Add Column
Add Row To DataFrame - using concat,Append
Order By Operation
Sort Values
Group by operation
Pivot Table
Date/ Time Functionality
Example Manipulating DataFrame

Matplotlib :

line graph
bar Plot
scatter plot
pie chart
other function

Numpy :

Introduction to Numpy
Creating Arrays , Indexing , Slicing
Data Types
Copy vs View
Array Shape & Reshape
Arrays Split & Joins
Arrays Filter
Seaborn Model

Data Engineering in Python :

Handling Missing Data
Techniques to inpute missing Values
Meaningful Data transformation
Encoding Data
Data Visualization in Python
Read Json , CSV's, excels

Term 2

In this term, you will learn how to ace MySQL, AWS, Tools & IDE’S

Module 3:- SQL/ETL

MYSQL :

DBMS & RDBMS
Data Types
DQL
DDL
DML
TCL
DCL
Key Constraints
Operators
Clouses
Aggregate Functions
Indexes
Views
Triggers
JOINS
Sub Queries & Nested Queries

Module 4:- AWS

Use of AWS
Cloud computing models
S3
AWS Data Pipeline
EMR
AWS Glue
Athena
Redshift

Module 5:- Tools

Azure devops :

Use Of Devops
CI/ CD Pipeline
work item
sprints
repository
state of task
Repose Clone
pull request

Git :

Use of Git
feature branch
clone
Add
Commit
Push

IDE :

PowerBI :
- Dashboards
- Application
DBeaver :
- Connection Process
- DB Manipulation
Jupyter Notebook :
- Google Colaboratory
- Pycharm

Term 3

In this term, you will learn how to ace Framework and industry projects

Module 6:- Framework

Pyspark :

Use of Pyspark For Data Science
Spark Session & RDD
Timestamp
Schema
Parallelize
Broadcast Variable
Create DataFrame
Transformations & actions
Empty DataFrame
Structure type and structure field
Select
Collect
WithColumn
Where & Filter
Drop & Drop Duplicate
orderby and sortby
Groupby
Joins
union and union all
union byname
map , flatmap
Sample by vs Sample
Pivote
maptype
Aggregate Functions
Windows Function
Read and Write in CSV
When
Split
collect
Row number
dense rank

Module 7:- Final Projects and course wrap-up

1) E-Commerce

2) Banking Domain

0 +

HOURS OF LIVE LEARNING

0 +

Live Projects

0 +

HOURS OF VIDEO LEARNING

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Detailed Course Syllabus & Trainer List

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Join our comprehensive Data Science course and qualify for top industry roles, including:

Data Engineer: Building and maintaining data pipelines, and working with data storage solutions and tools such as AWS Glue, Redshift, and MySQL.

Quantitative Analyst: Applying quantitative techniques to financial and business data, leveraging Python for data manipulation and analysis.

Data Analyst: Performing data analysis and visualization using tools like Pandas, Matplotlib, and PowerBI, and creating actionable insights from data.

Cloud Data Engineer: Working with cloud services and data pipelines, including AWS services like S3, EMR, and Athena.

DevOps Engineer: Implementing CI/CD pipelines and managing version control with Git, focusing on automation and integration in data engineering projects.