The Data Science with Python Certification Course equips learners with essential knowledge to leverage Python for data analysis, machine learning, and data visualization. Ideal for aspiring data scientists, analysts, and developers, this training helps build expertise in Python programming, statistical analysis, and machine learning libraries. Earning this certification enhances your profile in data science and analytics domains.

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Instructor led live online Classes

Data Science with Python Certification Course

Instructor led live online Training (Weekday/Weekend)

Why Enroll in the Data Science with Python Certification Course?

Gain In-Demand Skills: Learn to use Python for data analysis, visualization, and predictive modeling.
Career Advancement: Become a certified Data Science professional, highly valued across industries.
Hands-On Experience: Practice with real-time projects, datasets, and case studies.
Comprehensive Curriculum: Covers Python programming, pandas, NumPy, Matplotlib, and scikit-learn.
Recognized Certification: Boost your credibility in analytics, machine learning, and AI roles.

Course Description

This course validates your skills in using Python for data wrangling, statistical modeling, and predictive analytics.

1. Aspiring Data Scientists and Analysts. 2. Developers and Programmers interested in analytics. 3. Professionals aiming to enter the data science domain.

1. Provides hands-on labs and practical assignments. 2. Includes certification-focused exercises and mock exams. 3. 24/7 technical support and expert guidance.

What you'll learn

Python Programming Basics: Data types, functions, loops, and file handling.

Data Analysis with Pandas and NumPy.

Data Visualization using Matplotlib and Seaborn.

Exploratory Data Analysis and Statistical Methods.

Machine Learning with scikit-learn: Regression, classification, and clustering.

Model Evaluation and Optimization.

Requirements

Basic Programming Knowledge: Familiarity with any programming language is helpful.
No Prior Data Science Experience Required.

Curriculum Designed by Experts

Overview of Python
The Companies using Python
Different Applications where Python is Used
Discuss Python Scripts on UNIX/Windows
Values, Types, Variables
Operands and Expressions
Conditional Statements
Loops
Command Line Arguments
Writing to the Screen
What is Data Science?
What does Data Science involve?
Era of Data Science
Business Intelligence vs Data Science
Life cycle of Data Science
Tools of Data Science

Creating “Hello World” code
Variables
Demonstrating Conditional Statements
Demonstrating Loops

Data Analysis Pipeline
What is Data Extraction?
Types of Data
Raw and Processed Data
Data Wrangling
Python files I/O Functions
Numbers
Strings and related operations
Tuples and related operations
Lists and related operations
Dictionaries and related operations
Sets and related operations

Tuple - properties, related operations, compared with the list
List - properties, related operations
Dictionary - properties, related operations
Set - properties, related operations

Functions
Function Parameters
Global Variables
Variable Scope and Returning Values
Lambda Functions
Object Oriented Concepts
Standard Libraries
Modules Used in Python
The Import Statements
Module Search Path
Package Installation Ways
Errors and Exception Handling
Handling Multiple Exceptions

Lambda function in Python
Errors and Exceptions in Python
Packages and Modules in Python
Functions - Syntax, Arguments, Keyword Arguments, Return Values
Sorting - Sequences, Dictionaries, Limitations of Sorting

Data Analysis
NumPy - arrays
Operations on arrays
Indexing, slicing, and iterating
Reading and writing arrays on files
Pandas - data structures & index operations
Reading and Writing data from Excel/CSV formats into Pandas
Metadata for imported Datasets
Matplotlib library
Grids, axes, plots
Markers, colors, fonts, and styling
Types of plots - bar graphs, pie charts, histograms
Contour plots

NumPy library - Creating NumPy array, operations performed on NumPy array
Pandas library - Creating series and data frames, Importing and exporting data
Matplotlib library - Using Scatterplot, histogram, bar graph, a pie chart to show information, Styling of Plot

Basic Functionalities of a data object
Merging of Data objects
Concatenation of data objects
Types of Joins on data objects
Exploring and analyzing datasets
Analysing a dataset

Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples(), GroupBy operations, Aggregation, Concatenation, Merging and joining

What is Machine Learning?
Machine Learning Use-Cases
Machine Learning Process Flow
Machine Learning Categories
Linear regression
Gradient descent

Linear Regression – Boston Dataset

What are Classification and its use cases?
What is a Decision Tree?
Algorithm for Decision Tree Induction
Creating a Perfect Decision Tree
Confusion Matrix
What is Random Forest?

Implementation of Logistic Regression, Decision Tree, Random Forest algorithms

Introduction to Dimensionality
Why Dimensionality Reduction
PCA
Factor Analysis
Scaling dimensional model
LDA

Implementing PCA
Scaling dimensional model
Implementing LDA

What is Naïve Bayes?
How Naïve Bayes works?
Implementing Naïve Bayes Classifier
What is a Support Vector Machine?
Illustrate how Support Vector Machine works
Hyperparameter Optimization
Grid Search vs. Random Search
Implementation of Support Vector Machine for Classification

Implementation of Naïve Bayes, SVM algorithms

What is Clustering & its Use Cases?
What is K-means Clustering?
How does the K-means algorithm works?
How to do optimal clustering
What is C-means Clustering?
What is Hierarchical Clustering?
How does Hierarchical Clustering work?

Implementing K-means Clustering
Implementing Hierarchical Clustering

What are Association Rules?
Association Rule Parameters
Calculating Association Rule Parameters
Recommendation Engines
How do Recommendation Engines work?
Collaborative Filtering
Content-Based Filtering

Implementing Apriori Algorithm
Performing Market Basket Analysis

What is Reinforcement Learning?
Why Reinforcement Learning?
Elements of Reinforcement Learning
Exploration vs. Exploitation dilemma
Epsilon Greedy Algorithm
Markov Decision Process (MDP)
Q values and V values
Q – Learning
Values

Calculating Reward
Discounted Reward
Calculating Optimal quantities
Implementing Q Learning
Setting up an Optimal Action

What is Time Series Analysis?
Importance of TSA
Components of TSA
White Noise
AR model
MA model
ARMA model
ARIMA model
Stationarity
ACF & PACF

Checking Stationarity
Converting non-stationary data to stationary
Implementing Dickey-Fuller Test
Plotting ACF and PACF
Generating the ARIMA plot

What is Model Selection?
Need for Model Selection
Cross Validation
What is Boosting?
How do Boosting Algorithms work?
Types of Boosting Algorithms
Adaptive Boosting

Performing Cross Validation
Implementing AdaBoost using Python

What is Exploratory Data Analysis?
EDA Techniques
EDA Classification
Univariate Non-graphical EDA
Univariate Graphical EDA
Multivariate Non-graphical EDA
Multivariate Graphical EDA
Heat Maps

Implementing Graphical EDA Techniques
Implementing Non-Graphical EDA Techniques

Basics of database management
Python MySql
Create database
Create a table
Insert into table
Select query
Where clause
OrderBy clause
Delete query
Drop table
Update query
Limit clause
Join and Self-Join
MongoDB (Unstructured)
Insert_one query
Insert_many query
Update_one query
Update_many query
Create_index query
Drop_index query
Delete and drop collections
Limit query

CRUD operations using Python MySql and MongoDB

Data Visualization
Business Intelligence tools
VizQL Technology
Connect to data from the File
Connect to data from the Database
Basic Charts
Chart Operations
Combining Data
Calculations

Connecting to data from File, Database, and Server
Performing operations on Hierarchies, Data Granularity and Highlighting feature
Creating calculated fields using basic functions
Defining LOD expressions
Creating Parameters
Performing User Input and What-if analysis

Trend lines
Reference lines
Forecasting
Clustering
Geographic Maps
Using charts effectively
Dashboards
Story Points
Visual best practices
Publish to Tableau Online

Analyzing data using techniques including Forecasting, Trend Lines, Reference Lines, Clustering, and Geographic Maps
Building Dashboard Layout and Formatting
Building Story points

Predict the species of Plant

Analyze the data
Predict the plant species

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Data Science with Python Certification Course

Read Review

Instructor led live online Classes

Data Science with Python Certification Course