What if the participants are unable to attend any class?

If the participants are unable to attend a class, they have the option of attending the missed lecture through another batch or they can attend the recorded class that shall be available in their LMS.

Will Edureka provide placement assistance?

Participants will be given a resume builder tool in their LMS through which they will be able to create a good resume in 3 easy steps. They will be given unlimited access to templates for different roles and designations.

Can the participants attend a demo lecture?

Participants can view the sample class recording. It will give them a clear insight as to how the classes will be conducted, quality of instructors and the level of interaction in the classes.

Who will be the course instructors?

All the Edureka instructors are professionals from the Industry and subject matter experts. They have a minimum of 10-12 years of relevant IT experience. They are given training by Edureka to educate the participants better.

What if the participants have more queries?

If the participants have any queries, they can contact the officials at +91 98702 76459/1844 230 6365 (US Toll-Free Number) or drop a mail at sales@edureka.co

How will the practicals be executed?

Participants can do their assignments/case studies using Jupyter Notebook. It will be installed on their Cloud Lab environment, access details of which will be given on their LMS. They will be able to access their Cloud Lab environment from a browser.

What is the duration of the course?

Data Science with Python Certification Course by Edureka includes 42 hours of Online Live Instructor-led Classes.

What is the purpose of CloudLab?

CloudLab is a pre-installed cloud-based Jupyter Notebook with Python packages on the cloud-lab environment. It is provided by Edureka as a part of the Python Certification Course in which the participants can execute in-class demos and work on real-life projects.

How many case studies are included in the course?

This course includes 40 case studies which will enhance the learning experience of the participants. They will also be given 4 Projects to improve their implementation skills.

How are the batches divided?

The batches are divided as per weekend and week day classes. The weekend batch includes 14 sessions of 3 hours each and Weekday batch includes 21 sessions of 2 hours.

Data Science with Python Certification Course by Edureka: Fee, Duration, How to Apply

Data Science with Python Certification Course

Edureka

Develop your career in data science by enrolling for the Data Science with Python Certification Course by Edureka.

Online

6 Weeks

₹ 19795 21995

Important Dates

14 Dec, 2024

Course Commencement Date

particular	details
Medium of instructions English	Mode of learning Self study, Virtual Classroom	Mode of Delivery Video and Text Based	Frequency of Classes Weekends

particular

details

                                    Medium of instructions
                                    English

                                    Mode of learning
                                    Self study, Virtual Classroom

                                    Mode of Delivery
                                    Video and Text Based

                                    Frequency of Classes
                                    Weekends

Introduction to Data Science and ML using Python

Topics

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

Hands On/Demo

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

Data Handling, Sequences and File Operations

Topics

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

Hands On/Demo

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

Deep Dive – Functions, OOPs, Modules, Errors, and Exceptions

Topics

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

Deep Dive – Functions, OOPs, Modules, Errors and Exceptions

Hands On/Demo

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

Introduction to NumPy, Pandas and Matplotlib

Topics

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

Hands On/Demo

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

Data Manipulation

Topics

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

Hands On/Demo

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

Introduction to Machine Learning with Python

Topics

Revision (numpy, Pandas, scikit learn, matplotlib)
What is Machine Learning?
Machine Learning Use-Cases
Machine Learning Process Flow
Machine Learning Categories
Linear regression
Gradient descent

Hands On/Demo

Linear Regression – Boston Dataset

Supervised Learning - I

Topics

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

Hands On/Demo

Implementation of Logistic Regression, Decision Tree, Random Forest algorithms

Dimensionality Reduction

Topics

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

Hands On/Demo

Implementing PCA
Scaling dimensional model
Implementing LDA

Supervised Learning - II

Topics

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

Hands On/Demo

Implementation of Naïve Bayes, SVM algorithms

Unsupervised Learning

Topics

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

Hands On/Demo

Implementing K-means Clustering
Implementing Hierarchical Clustering

Association Rules Mining and Recommendation Systems

Topics

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

Hands On/Demo

Implementing Apriori Algorithm
Performing Market Basket Analysis

Reinforcement Learning

Topics

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

Hands On/Demo

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

Time Series Analysis

Topics

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

Hands On/Demo

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

Model Selection and Boosting

Topics

What is Model Selection?
The need for Model Selection
Cross-Validation
What is Boosting?
How Boosting Algorithms work?
Types of Boosting Algorithms
Adaptive Boosting

Hands On/Demo

Performing Cross Validation
Implementing AdaBoost using Python

Statistical Foundations (Self-Paced)

Topics

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

Hands-on

mplementing Graphical EDA Techniques
Implementing Non-Graphical EDA Techniques

Database Integration with Python (Self-Paced)

Topics

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

Hands-on

CRUD operations using Python MySql and MongoDB

Data Connection and Visualization in Tableau (Self-Paced)

Topics

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

Hands-on

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

Advanced Visualizations (Self-Paced)

Topics

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

Hands-on

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

In-Class Project (Self-Paced)

Topics

Predict the species of Plant

Hands-on

Analyze the data
Predict the plant species

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Data Science with Python Certification Course

Online

6 Weeks

₹ 19795 21995

Quick Facts

Course overview

The highlights

Program offerings

Course and certificate fees

Fees information

certificate availability

certificate providing authority

Who it is for

Eligibility criteria

What you will learn

The syllabus

Introduction to Data Science and ML using Python

Topics

Hands On/Demo

Data Handling, Sequences and File Operations

Topics

Hands On/Demo

Deep Dive – Functions, OOPs, Modules, Errors, and Exceptions

Topics

Deep Dive – Functions, OOPs, Modules, Errors and Exceptions

Hands On/Demo

Introduction to NumPy, Pandas and Matplotlib

Topics

Hands On/Demo

Data Manipulation

Topics

Hands On/Demo

Introduction to Machine Learning with Python

Topics

Hands On/Demo

Supervised Learning - I

Topics

Hands On/Demo

Dimensionality Reduction

Topics

Hands On/Demo

Supervised Learning - II

Topics

Hands On/Demo

Unsupervised Learning

Topics

Hands On/Demo

Association Rules Mining and Recommendation Systems

Topics

Hands On/Demo

Reinforcement Learning

Topics

Hands On/Demo

Time Series Analysis

Topics

Hands On/Demo

Model Selection and Boosting

Topics

Hands On/Demo

Statistical Foundations (Self-Paced)

Topics

Hands-on

Database Integration with Python (Self-Paced)

Topics

Hands-on

Data Connection and Visualization in Tableau (Self-Paced)

Topics

Hands-on

Advanced Visualizations (Self-Paced)

Topics

Hands-on

In-Class Project (Self-Paced)

Topics

Hands-on

Admission details

Filling the form

How it helps

FAQs

What if the participants are unable to attend any class?