WEEK 1:(Introduction to Data Science)

Day 1:

Introduction to Data Science

Different Sectors Using Data Science

1.Implementation of installation of libraries.

Day 2:

Purpose and Components of Python

Data Science Overview, Data Analytics Overview

Day 3:

Data Analytics Process, Knowledge Check

2.implementation of data analysis process.

Day 4:

Exploratory Data Analysis (EDA), EDA-Quantitative Technique

Day 5:

EDA - Graphical Technique, Data Analytics Conclusion or Predictions

3. implement EDA.

Day 6: Practical Day / Personality Development/Repeated Class/Issues/Guest

WEEK 2:(Pandas Library)

Day 1:

Data Analytics Communication, Data Types for Plotting, Data Types and Plotting

1.Implementation of installation of Pandas.

Day 2:

Data Analysis process using panda library of python.

Day 3:

Functions and methods used in pandas for the data analysis process.

2.implementation of panda’s methods.

Day 4:

Understanding the concept of DataFrame and series of pandas.

Day 5:

Pandas implementation with csv files.

3. work on dataframe and series.

Day 6: Practical Day / Personality Development/Repeated Class/Issues/Guest

WEEK 3:(NumPy Library)

Day 1:

Data Analytics through Numpy.

1.Implementation of installation of NumPy.

Day 2:

Data Analysis process using NumPy library of python.

Day 3:

Functions and methods used in NumPy for the data analysis process.

2.implementation of Numpy function for data analysis process.

Day 4:

Understanding the concept of Numpy in comparison with Pandas.

Day 5:

NumPy implementation with csv files.

3. work on NumPy Methods.

Day 6: Practical Day / Personality Development/Repeated Class/Issues/Guest

WEEK 4:(Matplotlib Library)

Day 1:

Data Visualization through Matplotlib and installation process.

1.Implementation of installation of matplotlib.

Day 2:

Data Visualization process using Matplotlib library of python.

Day 3:

Functions and methods used in Matplotlib for the data Visualization process.

2.implementation of matplotlib function for data analysis process.

Day 4:

Understanding the concept of Matplotlib with oops.

Day 5:

Matplotlib implementation with csv files.

3. work on Matplotlib Methods.

Day 6: Practical Day / Personality Development/Repeated Class/Issues/Guest

WEEK 1:( Machine Learning )

Day 1:

Introduction about Machine Learning.

1.Practical of linear regression.

Day 2:

Supervised learning algorithm and unsupervised algorithm-linear regression, Logistic Regression

Day 3:

Polynomial regression, decision tree

2.Practical of Logistic regression.

Day 4:

Random forest, Ridge regression.

Day 5:

Lasso Regression and back propagation.

3. practical of random forest and ridge regression.

Day 6: Practical Day / Personality Development/Repeated Class/Issues/Guest

WEEK 2:(Classification algorithm)

Day 1:

Logistic classification.

1.practical of logistic  and decision tree algorithm.

Day 2:

Decision tree classification.

Day 3:

Random forest classification.

2.implementation of random forest.

Day 4:

K-nearest neighbour classification.

Day 5:

Naïve bays classification and Support vector machine classification.

3. work on project using classification algorithms.

Day 6: Practical Day / Personality Development/Repeated Class/Issues/Guest

WEEK 3:(Clustering algorithm)

Day 1:

Affinity propagation clustering.

1.practical of affinity and BIRCH clustering.

Day 2:

Agglomerative clustering.

Day 3:

BIRCH clustering and DBSCAN clustering.

2.implementation of k-means and means shift.

Day 4:

K-means clustering and means shift clustering.

Day 5:

 Mini-batch Clustering, optics clustering and spectra clustering.

3. work on project using clustering algorithms.

Day 6: Practical Day / Personality Development/Repeated Class/Issues/Guest

WEEK 4:(Web Scrapping)

Day 1:

Introduction about web scrapping and its libraries.

1.practical of web scrapping and installation.

Day 2:

Web scrapping through BS4 library of python for web scrapping.

Day 3:

Web scrapping through BeautifulSoup library of python for web scrapping.

2.implementation of project through BS4 , selenium and BeautifulSoup.

Day 4:

Web scrapping through Selenium library of python for web scrapping.

Day 5:

Project work on web scrapping.

3. work on project using web scrapping libraries.

Day 6: Practical Day / Personality Development/Repeated Class/Issues/Guest

                                        DATA SCIENCE COURSE

Data Science course duration

2 months

Data Science Course Mode

Offline/Online

Data Science Course Fee

15000/-

Data Science projects

Work on Live Projects of Data science.

Free with Data Science Course

Project Report, Certificate, Synopsis and PPT.

Also provide International Data Science Certificate