Data analytics is the process of examining, cleaning, transforming, and modeling data to uncover meaningful insights, draw conclusions, and support decision-making. It involves the use of various techniques and tools to analyze large sets of data
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Day 1.
MODULE: PYTHON ESSENTIALS INTRODUCTION
What is Python…?
A Brief history of Python
Why Should I learn Python…?
Career opportunities after learning Python
Installing Python
Overview of Anaconda and different IDEs
Day 2.
How to execute Python program
Write your first program
Variables
Different data types
Different Operator
Immutable and Mutable
Object Referencing and Identity
Day 3.
Strings and escape char
String Formatting ( f string)
formats() for string formatting
Input Statement
Indexing, Slicing & Dicing
List
Day 4
Tuple
Dictionary
Sets
Operators in details
Day 5
IF_ELSE
Basic for loops
for loops
Day 6
while loops
nested loops
infinite loops
loops deep dive
Break statement
Continue statement
Day 7
List
List methods
List Comprehension
List Comprehension with If & Else
Day 8
String, String methods
Tuple, Tuple Methods
Dictionary, Dictionary methods
Tuple, Dictionary Comprehension
Day 9
Function in python
Lambda
MAP
Filter
Reduce
Zip
Accumulate
Decorators
Day 10
math_stats_lib
Datetime
Classes in Date time
RegEx
Day 11
Iterable and Iterator
Generator
Generator vs List
Enumerate
Modules
Day 12
OOPS Concept
Class & Objects
__init__
__str__
Abstraction
Inheritance
Super()
Encapsulation
Polymorphism
__init__ module
Day 13
File Handling in Python
File objects and Modes of file operations
Reading, writing and use of ‘with’ keyword
read(), readline(), readlines(),write(),writeline()
Day 14
⦁ Exception Handling
⦁ Understanding exceptions
⦁ try, except, else and finally
⦁ raising exceptions with: raise, assert
Day 15
logging and debugging
Python Connection with SQL Server
Day 16
NumPy Overview
Properties, Purpose, and Types of ndarray
Class and Attributes of ndarray Object
Basic Operations: Concept and Examples
Initializing arrays: random, ones, zeros
Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
Shape Manipulation
Day 17
Pandas
Day 18
Pandas
Day 19
Data analysis – Visualization using Pandas, Matplotlib, Plotly & Seaborne
Day 20
Python EDA project
Day 21
Getting to Know APIs
SOAP vs REST
Calling Your First API Using Python
We’re going to create a API
Introduction to Git
Useful Git Commands
Day 22
Introduction to Big Data
Apache Spark Introduction
PySpark Introduction
Platforms to use PySpark
Day 23
PySpark Data frame
Reading The Dataset
Checking the Data types of the Column(Schema)
Selecting Columns And Indexing
Check Describe option similar to Pandas
Adding Columns
Dropping columns
Renaming Columns
Dropping Columns
Dropping Rows
Various Parameters in dropping functionalities
Handling Missing values by Mean, Median and Mode
Filter Operation
&,|,==
~
PySpark Group By And Aggregate Functions
Day 24
Pyspark Use Case
End of Course
Thanks
Module 1: INTRODUCTION TO POWER BI
Module 2: CREATING POWER BI REPORTS, AUTO FILTERS
Module 3: REPORT VISUALIZATIONS and PROPERTIES
Module 4 : CHART AND MAP REPORT PROPERTIES
Module 5 : HIERARCHIES and DRILLDOWN REPORTS
Module 6 : POWER QUERY & M LANGUAGE – Part 1
Module 7 : POWER QUERY & M LANGUAGE – Part 2
Module 8 : DAX EXPRESSIONS – Level 1
Module 9 : DAX EXPRESSIONS – Level 2
Module 10 : POWERBI DEPLOYMENT & CLOUD
Module 11 : POWER BI CLOUD OPERATIONS
Module 12 : IMPROVING POWER BI REPORTS
Module 13 : INSIGHTS AND SUBSCRIPTIONS
Duration 5-6 months.
Gathering relevant data from various sources, such as databases, spreadsheets, sensors, and more.
Investigating the data through exploratory analysis to understand its characteristics, distribution, and potential patterns.
Analyzing the results and deriving actionable insights to inform business decisions or strategies.
Creating visual representations of the data, such as charts, graphs, and dashboards, to make complex information more accessible and understandable.
Applying statistical and machine learning techniques to build models that can make predictions or identify patterns within the data.
Preprocessing and refining the data to ensure accuracy and eliminate any errors or inconsistencies
We are making our students masters in every aspects Whether it is communication skills, Data Visualization or so on.
Effective Data Collection and Statistical Analysis is the core strength of Data Analytics
Creating visual representations of data to communicate complex findings effectively. Scientists use tools like charts, graphs
Effectively communicating findings and insights to non-technical stakeholders, such as business leaders or decision-makers.
Staying updated on the latest developments in data science, machine learning, and related fields.
Fine-tuning machine learning models to improve their performance, often through hyperparameter tuning
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