Python Data Science Course

BY
Intellipaat

Master Python and learn coding in running Linux, Mac and Windows with ease with Python Data Science Course and Training programme from Intellipaat.

Mode

Online

Fees

₹ 20007

Quick Facts

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

Course overview

Intellipaat is a global professional online training provider that imparts numerous courses and certifications to master data science analytic skills through real-world projects in muti domains like e-commerce, retail, finance and many other areas. The programme enhances all the skills of the candidate to uplift the skills and to transfer the path of technology to a digital framework.

In the digital world of technical enhancement Data Science and Python have much more importance to improve the data in various forms and to upgrade technology by most faster means for the most updated technology. To help clients for upskilling their workforce and keep them in sync with changing technology and digital landscape. Data science has core competencies to deploy data for visualization, data science, data cleaning and also the domain of data analytics.

Intellipaat is a global online professional training provider that consists of professors and industrial experts with over more than decades of experience to evaluate learner progress and offer industry-recognized certifications. Upon the completion of the programme, Python Data Science Course and Training candidates will be able to hire in the data science domain with top salaries.

The highlights

  • Online/blended programme by Intellipaat academy
  • Course completion certificate by Intellipaat academy 
  • 39 hours instructor-led programme 
  • 24x7 lifetime support and access
  • 24 hours self-paced programme with certification and job assistance
  • 50 hours of project assistance by industrial experts and exercises

Program offerings

  • Online videos
  • Quiz
  • Practice projects
  • Daily assessment
  • Case studies
  • Instructor-led training
  • Project work
  • Exercise

Course and certificate fees

Fees information
₹ 20,007

Data Science with Python Certification Training Course Fees

Type of training

Amount in INR

Corporate training

NA

Online Classroom

Rs. 20,007

Note: For Online Classroom training there is a 10% discount available

certificate availability

Yes

certificate providing authority

Intellipaat

Who it is for

The course is benefited for candidates who are:

  • Project Managers
  • BI Managers
  • Analytics Professionals 
  • Software  Developers
  • ETL Professionals
  • Big Data Professionals 
  • Professionals willing to seek career options in Python

Eligibility criteria

Work experience:

There is no work experience required to take part in this programme. Freshers and experienced candidates can apply.

Education:

There are no prerequisites for education. Therefore everyone who has an interest in learning python and data science can apply.

Certification qualifying details:

After completion of the Intellipaat training programme, working on real-world projects, quizzes, assignments, practice assessments candidate will have a qualifying exam and the certificate is awarded for course completion only if he/she gets at least 60% in the qualifying exam

What you will learn

Machine learning Data science knowledge Knowledge of python Knowledge of data visualization Knowledge of applied statistics

After completion of the Python Data Science Course learners can be skilled in the following areas:

  • Learners will have enhanced skills in real-world data science projects 
  • Machine learning for developing efficiency without any explicit programming
  • Advanced numerical analysis for probability and statistical calculation to calculate the efficiency
  • C++ language is for developing the efficiency of operating systems and browsers
  • Programming for developing a python platform as a domain of data science

The syllabus

Module-1

Introduction to Data Science using Python
  • What is data science and what does a data scientist do?
  • How python is deployed for data science applications
  • Introduction to python programming language
  • Various steps in the data science process like data wrangling, data exploration and selecting the model
  • Python installation, Anaconda python distributions for Windows, Linux and Mac
  • Important python features, how is python different from other programming languages
  • Various examples of data science in Industries
  • How to run a sample python script, Python IDE working mechanism
  • Python variables, data types and key-words
  • Running some python basic commands
Exercise
  • Installing Python anaconda for the windows, Linux and Mac

Module-2

Python basic constructs
  • Introduction to a basic construct in Python
  • Loop and control statements like a break, if, for, continue, else, range() and more.
  • Python built-in data types
  • A basic operation in python
  • Understanding indentation like tabs and spaces
Exercises
  • Write a class
  • Use Lambda expressions
  • Write your first python programme
  • Write a Python function (with and without parameters)
  • Create a member function and a variable
  • Create an object and write a for loop to print all odd numbers

Module-3

Maths for Ds_Statistics and Probability
  • Correlation
  • Regression
  • Anova
  • Central Tendency
  • Variability
  • Hypothesis Testing
  • Baye’s theorem
  • The sum rule, Conditional probability, and the Product rule
  • Joint probabilities
  • Probability Definitions and Notations
Exercise
  • Analyzing both categorical and quantitative data
  • Focusing on specific case studies to help solidify the week’s statistical concepts

Module-4

OOPS in Python
  • Understanding the OOP paradigm like encapsulation, inheritance, polymorphism and abstraction
  • Lambda expressions
  • Classes and Objects
  • What are access modifiers, instances, class members
  • Function Parameter and return type functions
Exercise
  • Write a python programme and incorporating the OOP concepts

Module-5

NumPy for mathematical computing
  • Introduction to mathematical computing in Python
  • What are arrays and matrices, array indexing, array math, Inspecting a NumPy array, Numpy array manipulation
Exercise
  • Creating an array using ND-array
  • Calculating standard deviation on an array of numbers and how to import the calculating correlation between two variables
  • How to import NumPy module

Module-6

Scipy for scientific computing
  • Introduction to scipy, building on top of numpy
  • Various sub-packages for scipy like Signal, Integrate, FFtpack, Cluster, Optimize, stats and more, Bayes Theorem with scipy
  • What are the characteristics of scipy
Exercise
  • Applying the Bayes theorem on the given data set
  • Importing of scipy

Module-7

Data manipulation
  • What is data manipulation using Panda’s library
  • A series object in pandas
  • Dataframe in pandas
  • Numpy dependency of the panda's library
  • How to merge data objects
  • Concatenation and various types of joins on data objects, exploring a dataset
  • Loading and handling data with pandas
Exercise
  • Cleaning dataset, Manipulating dataset, visualizing the dataset
  • Doing data manipulation with Pandas by handling tabular datasets that include variable types like float, integer, double and others

Module-8

Data visualization with Matplotlib
  • Introduction to Matplotlib
  • Matplotlib API
  • Using Matplotlib for plotting graphs and charts like Scatter, Bar, Pie, line < histogram and more
Exercise
  • Subplots and Pandas built-in data visualization
  • Deploying matplotlib for creating pie, scatter, line and histogram

Module-9

Machine learning using Python
  • Introduction to machine learning
  • Types of machine learning and workflow of Machine Learning 
  • The need for Machine Learning
  • What is Unsupervised learning?
  • What is supervised learning?
  • Use Cases in Machine Learning, its various algorithms
Exercise
  • Demo on ML algorithms

Module-10

Supervised learning
  • Step by step calculation of Linear regression
  • What is linear regression
  • Linear regression in python
  • What is classification
  • Logistic Regression
  • Decision Tree, Confusion Matrix, random Forest, naïve Bayes classifier (self-paced), Support Vector Machine(self-paced), xgboost(self-paced)

Module-11

Unsupervised Learning
  • Introduction to unsupervised learning
  • What is clustering
  • Use cases of unsupervised learning
  • Types of clustering
  • What is K-means clustering
  • Association rule mining
  • Step by step calculation of the k-means algorithm
  • Apriori Algorithm
Exercise
  • Practice on Apriori
  • Setting up the Jupyter notebook environment
  • Practice on k-means using Scikit
  • Loading on a dataset in jupyter
  • Algorithms in Scikit-Learn package for performing machine Learning

Module-12

Python Integration with Spark
  • Introduction to pyspark
  • Pyspark fundamentals
  • Who uses pyspark, the need for spark with python
  • Use-cases pyspark and demo
  • An advantage over MapReduce, pyspark
  • Pyspark installation
Exercise
  • Demonstrating loops and Conditional statements
  • List-operations, related properties
  • Set- properties, associated operations, dictionary-operations, related topics
  • Tuple_related operations, properties, list, etc.

Module-13

Dimensionality Reduction
  • Introduction to Dimensionality
  • PCA
  • LDA
  • Factor Analysis
  • Why Dimensionality Reduction
Exercise
  • Practice Dimensionality reduction Techniques: PCA, Factor Analysis, t-SNE, Random Forest, Forward, and Backward feature

Module-14

Time-series Forecasting
  • White Noise
  • ARMA Model
  • AR Model
  • MA Model
  • ARIMA Model
  • ACF and PACF
  • Stationarity
Exercise
  • Create MA Model
  • Create an ARMA model
  • Create an AR model

Projects

  • Analyzing the Trends of COVID-19 with Python
  • Analyzing the naming trends using Python
  • Performing analysis on customer Chrn data set
  • Python web-scraping for data science
  • OOPS in Python
  • Working with NumPy
  • Visualizing and Analyzing customer churn using python
  • Building Models with help of Machine learning Algorithm

Admission details


Filling the form

Follow the instructions to take part in this programme Python Data Science Course :

Step-1: Go to the course website and click enrol now

Step-2: After enrolling there appears an application form where candidates need to fill and accept all the terms and conditions.

Step-3: Candidate will get verified with One time Password and get verified. Then after the candidate should pay the fee.

Step-4: Make the payment. Attach the receipt with the application form.

Step-5: You will get a confirmation message and certificate verification.

How it helps

With the programme Python Data Science Course students will be able to transition for higher positions with all the uplifted skills and knowledge. The programme offers job assistance where it helps its candidates to get hired in their desired jobs and the qualifications that are required for the organization.

The candidate will be provided extensive hands-on projects and rigorously evaluate the progress of learners progress and offer industry-recognized certificates. The training programmes can uplift their skills in the domains of Big Data, DataScience, Artificial Intelligence, Machine Learning, Programming, and 150 more technologies.

There will be instruction led trainer and lifetime access to the materials which will help students to take part in the course as many times as possible if he fails in getting a certificate. Since python is the best programming to get reliable and effective outcomes than Java, C++, and C languages this academy is making more flexibility to the students to give their students more and more.

Instructors

Mr Madhu Babu Cherukuri

Mr Madhu Babu Cherukuri
Data Scientist
Intel Corporation

FAQs

Will I get a certificate after the course if I did not get 60% in the qualifying test?

No, the candidate will get awarded only if he gets 60% in the qualifying test. It is compulsory for candidates to achieve this. 

What is the tenure of the programme?

There is no specific tenure for the programmer. Students can learn any time he/she wants.

Can I access the videos on mobile?

Yes, there is a mobile application for this academy and can access anywhere.

Can I access the videos if I could not pay the fee?

No, Your admission will be successful only after paying the fee.

Is there a separate fee for the application form?

No, all the fees you pay will be enough and there is no separate fee for the application form.

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