Do I need particular system requirements for this training?

No. You will be using a Cloud LAB environment for practical work. This environment will already house the required software to execute the practicals.

How will I work on the practicals?

The curriculum will provide you with Jupyter Notebook, pre-installed on your Cloud LAB to work on your practicals. The access details will be on the LMS.

Is there any way to go through a session if I miss it?

If you miss a lecture or class, don’t fret. You can easily attend another live batch or go through pre-recorded videos.

Does this training entail placement assistance?

Yes; Edureka will provide you with a resume-building software. Using this, you will be able to build a strong CV and ace placements.

Is there any way to contact Edureka?

You can contact Edureka for clearing any query or doubt. You can reach them at 18442306365/ 9870276459, or email them at sales@edureka.co.

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

Data Science with Python Certification Course

Edureka

Join the Data Science with Python Certification Course to understand machine learning’s mechanism and its implementation in Python.

Online

7 Weeks

₹ 19795 21995

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

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

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

Hands-on

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

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

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

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

Hands-on

Linear Regression – Boston Dataset

Supervised Learning - I

Topics

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?

Hands-on

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

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

Implementation of Naïve Bayes, SVM algorithms

Unsupervised Learning

Topics

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?

Hands-on

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 do Recommendation Engines work?
Collaborative Filtering
Content-Based Filtering

Hands-on

Implementing Apriori Algorithm
Performing Market Basket Analysis

Reinforcement Learning (Self-Paced)

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

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

Time Series Analysis (Self-Paced)

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

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

Hands-on

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

Implementing 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

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Data Science with Python Certification Course

Online

7 Weeks

₹ 19795 21995

Quick Facts

Course overview

The highlights

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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

Data Handling, Sequences and File Operations

Topics

Hands-on

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

Topics

Hands-on

Introduction to NumPy, Pandas, and Matplotlib

Topics

Hands-on

Data Manipulation

Topics

Hands-on

Introduction to Machine Learning with Python

Topics

Hands-on

Supervised Learning - I

Topics

Hands-on

Dimensionality Reduction

Topics

Hands-on

Supervised Learning - II

Topics

Hands-on

Unsupervised Learning

Topics

Hands-on

Association Rules Mining and Recommendation Systems

Topics

Hands-on

Reinforcement Learning (Self-Paced)

Topics

Hands-on

Time Series Analysis (Self-Paced)

Topics

Hands-on

Model Selection and Boosting

Topics

Hands-on

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

Admission details

Filling the form

How it helps

FAQs

Do I need particular system requirements for this training?

How will I work on the practicals?

Is there any way to go through a session if I miss it?