Data Science Training Chennai

Python Introduction :

• Introduction to Python
• History of Python
• Why Python?
• Python Environment Setup
• Comments in Python
• Variables in Python
• Operators in Python

Data Types :

• Input and Output in Python
• Data Types in Python
• Type Conversion
• Type Casting in Python
• Numeric Data type and its Methods
• Text Datatype and its Methods
• String Modification
• Strings are Arrays
• String Slicing
• Format String

Collections :

• Creating a List
• Accessing List Elements
• List Slicing
• Modifying Lists
• List Methods
• List Comprehension
• List Operations
• Creating a Tuple
• Accessing Tuple Elements
• Slicing Tuples
• Tuple Operations
• Tuple Methods
• Tuple Unpacking
• Creating a Set
• Properties of a Set
• Adding Elements to a Set
• Removing Elements from a Set
• Set Operations
• Set Membership
• Frozenset
• Common Set Methods
• Creating a Dictionary
• Accessing Dictionary Elements
• Adding and Modifying Dictionary Elements
• Removing Elements from a Dictionary
• Dictionary Methods
• Iterating Over a Dictionary
• Nested Dictionaries

Control Flow Statements :

1.Conditional Statements :

• If-Else Statement
• Elif Statement
• Nested IF Statement

2.Loops in Python :

• Understanding of Iterators and Iterables
• For Loop
• For Loop Using Range
• Nested For Loop
• While Loop

3. Control Transfer Statements :

• Break
• Continue
• Pass

Functions in Python :

• Built-in Functions
• User Defined Functions
• Define a function
• Calling a function
• Function Parameters
• Passing an Argument
• Arbitrary Arguments
• Keyword Arguments
• Arbitrary Keyword Arguments
• Default Parameter
• Lambda Function

File Handling :

• Opening a File
• Reading a File
• Writing to a File
• Appending to a File
• Closing a File
• File Methods
• Working With Text Files
• Delete a File
• Working Binary Files[videos, audios and images]
• Context Manager
• File Pointer
• Checking if File Exists
• Delete a Folder
• Working with CSV Files
• Working with ZIP Files
• Handling Exceptions in File Handling

Exception Handling :

• What is Exception?
• Common Built-in Exceptions
• Basic Syntax of Exception Handling
• Catching Multiple Exceptions
• Catching All Exceptions
• The else Clause
• The finally Clause
• Raising Exceptions

OOPS in Python :

Class and Objects :

• How to Define a Class?
• Creating a Objects
• Use of Self Keyword
• Methods and Attributes
• Constructor(__init__)

Inheritance :

• What is Inheritance?
• Types of Inheritance
• Single Inheritance
• Multiple Inheritance
• Multilevel Inheritance
• Hierarchical Inheritance
• Hybrid Inheritance

Polymorphism :

• What is Polymorphism?
• Method Overloading
• Method Overriding

Encapsulation:

• What is Encapsulation?
• Access Modifiers
• Using Public Members
• Using Private Members
• Using Protected Members

Abstraction :

• import the ABC module
• Abstract Base Class
• Abstract Class
• Abstract Method

Numpy

• Array operation
• Indexing and slicing
• Shape and reshape
• Data types
• Mathematical functions
• Statistical functions
• Random

Pandas

Data structures

• Series
• Data Frame

Data operations

• Creation
• Indexing and selecting data
• Filtering
• Sorting

Data manipulation

• Adding/removing columns and rows
• Merging

Data manipulation

• Adding/removing columns and rows
• Merging

Installation

SQL Languages

• Data Definition Language (DDL)
• Data Manipulation Language (DML)
• Data Query Language (DQL)
• Data Control Language(DCL)
• Transaction Control Language (TCL)

Constraints

• PRIMARY KEY
• UNIQUE
• FOREIGN KEY
• CHECK
• NOT NULL
• DEFAULT
• EXCLUDE

Clauses of SELECT statement

• FROM
• WHERE
• GROUP BY
• HAVING
• ORDER BY
• DISTINCT
• LIMIT/OFFSET

Subquery

JOINS

• INNER JOIN,
• OUTER JOIN
• (LEFT OUTER JOIN, RIGHT OUTER JOIN, FULL OUTER JOIN)
• SELF JOIN and CROSS JOIN

Windows Function

• ROW_NUMBER
• RANK
• DENSE_RANK
• LEAD
• LAG

• • Data collection
  • Data cleaning and preprocessing
  • Exploratory Data Analysis (EDA)
  • Model building
  • Model evaluation and deployment

Data Collection Techniques

• Collecting data using APIs
• Basics of web scraping with BeautifulSoup and Scrapy
• Managing databases with SQL
• Importing data from various file formats (CSV, Excel, JSON)

Data Cleaning and Wrangling

• Handling missing values (deletion and imputation)
• Managing outliers
• Transforming data (scaling and normalization)
• Merging and combining datasets

Handling Missing Data

• Understanding missing data patterns
• Techniques for handling missing data
• Assessing the impact of missing data on analysis

Feature Engineering

• Creating new features from existing data
• Binning and categorizing data
• Scaling and normalizing features
• Encoding categorical variables

Principles of Data Visualization

• Importance of visualization in data analysis
• Best practices for effective visualizations
• Choosing the right chart type

Visualization Tools

• Matplotlib, Seaborn (Python)
• ggplot2
• Customizing plots (colors, labels, legends)

Creating Basic Plots

• Bar charts
• Line charts
• Scatter plots
• Histograms

Advanced Visualization Techniques

• Heatmaps
• Pair plots
• Box plots and violin plots

Descriptive Statistics

• Measures of central tendency (mean, median, mode)
• Measures of dispersion (variance, standard deviation)
• Skewness and kurtosis

Probability Theory

• Basic probability concepts
• Conditional probability and Bayes’ Theorem

Probability Distributions

• Normal distribution
• Binomial distribution
• Poisson distribution
• Probability density functions (PDFs) and cumulative
• Distribution functions (CDFs)

Hypothesis Testing

• Null and alternative hypotheses
• Types of errors (Type I and Type II)
• p-values and statistical significance
• t-tests, chi-square tests, ANOVA

Confidence Intervals and P-Values

• Constructing confidence intervals
• Interpreting p-values
• Margin of error