Data Science with Python Certification

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Data Science with Python Certification

EduCerts Data Science with Python Certification Course is NASSCOM-accredited, meets industry requirements, and is authorized by the Government of India. This course will teach you crucial Python topics including data operations, file operations, and different Python libraries like Pandas, NumPy, and Matplotlib that are required for Data Science. This training is appropriate for both experts and novices. This Python for Data Science certification programmer will also teach you about Machine Learning, Recommendation Systems, and other Data Science ideas to help you get started in your Data Science career.

Why This Course

According to the US Bureau of Labor Statistics, there will be approximately 11.5 million additional Data Science positions by 2026.

After passing the required NASSCOM Assessment, you can claim rewards from the Government of India (GOI).

According to the US Bureau of Labor Statistics, the nationwide average pay for a data analyst is $119,563 per year.

Why Enroll In Course?

Data science career opportunities are expected to grow by 30% every year, and skill in Python programming and data science can open up a plethora of chances. The increased demand for qualified data scientists and machine learning engineers has driven more firms to adopt machine learning into their operations, allowing them to analyse and process data more quickly and effectively. Obtaining a Data Science and Machine Learning Certification from EduCerts is the best approach to land a lucrative job in this sector.

Training Features

Live Interactive Learning

Lifetime Access

24x7 Support

Hands-On Project Based Learning

Industry Recognized Certification

Cloud

Course Curriculum

Introduction to 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

Hands-on:

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

Skills You will Learn:

Basics of Python Programming
Command Line Parameters and Flow Control in Python

Sequences and File Operations

Topics:

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 list
List - properties, related operations
Dictionary - properties, related operations
Set - properties, related operations

Skills You will Learn:

Taking input from the user and performing operations on it
Data types in Python

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:

Functions - Syntax, Arguments, Keyword Arguments, Return Values
Lambda - Features, Syntax, Options, Compared with the Functions
Sorting - Sequences, Dictionaries, Limitations of Sorting
Errors and Exceptions - Types of Issues, Remediation
Packages and Module - Modules, Import Options, sys Path

Skills You will Learn:

Object Oriented Concepts
Python Functions, Standard Libraries and Modules
Handling Exceptions in Python

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 dataframes, Importing and exporting data
Matplotlib - Using Scatterplot, histogram, bar graph, pie chart to show information, Styling of Plot

Skills You will Learn:

Basic Functionalities of the NumPy library in Python
Basic Functionalities of the Pandas library in Python
Basic Functionalities of the Matplotlib library in Python

Data Manipulation

Topics:

Basic Functionalities of a data object
Merging of Data objects
Concatenation of data objects
Types of Joins on data objects
Exploring a Dataset
Analyzing a dataset

Hands-on:

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

Skills You will Learn:

Performing data manipulation using various functionalities of the Pandas library in Python

Introduction to Machine Learning with Python

Topics:

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

Hands-on:

Linear Regression – Boston Dataset

Skills You will Learn:

Machine Learning concepts
Machine Learning types
Linear Regression Implementation

Supervised Learning - I

Topics:

What is 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:

Implementation of Logistic regression, Decision tree, Random forest

Skills You will Learn:

Supervised Learning concepts
Implementing different types of Supervised Learning algorithms
Evaluating model output

Dimensionality Reduction

Topics:

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

Hands-on:

PCA
Scaling

Skills You will Learn:

Implementing Dimensionality Reduction Technique

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

Skills:

Supervised Learning concepts
Implementing different types of Supervised Learning algorithms
Evaluating model output

Unsupervised Learning

Topics:

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

Hands on:

Implementing K-means Clustering
Implementing Hierarchical Clustering

Skills:

Unsupervised Learning
Implementation of Clustering – various types

Association Rules Mining and Recommendation Systems

Topics:

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

Hands on:

Apriori Algorithm
Market Basket Analysis

Skills:

Data Mining using python
Recommender Systems using Python

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:

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

Skills:

Implement Reinforcement Learning using Python
Developing Q Learning model in Python

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:

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

Skills:

TSA in Python

Model Selection and Boosting

Topics:

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

Hands on:

Cross Validation
AdaBoost

Skills:

Model Selection
Boosting algorithm 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

Skills You will Learn:

Performing EDA on the dataset(s) in Python

Data Connection and Visualization in Tableau (Self-paced)

Topics:

Data Visualization
Business Intelligence tools
VizQL Technology
Connect to data from File
Connect to data from 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

Skills You will Learn:

Data Distribution using various charts in Tableau
Combining Data using Joins, Unions and Data Blending
Sorting, Filtering and Grouping Techniques
Table Calculations in Tableau

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

Skills You will Learn:

Advanced visualization techniques in Tableau
Building Dashboards and Stories in Tableau

Free Career Counselling

We are happy to help you 24/7

Certification

What is Future Skills Prime?

A skilling ecosystem fueled by a collaboration between the Ministry of Electronics and Information Technology, the Government of India, NASSCOM, and the IT sector. It aims to develop India into a global talent magnet for emerging technology. Future Skills Prime is one of the government's trillion-dollar Digital Economy initiative's lighthouse programmes.

How will I get the NASSCOM Future Skills certification?

To achieve a Joint Co-Branded Certificate of Completion from NASSCOM Future Skills Prime and CertHippo, you must successfully complete the modules. After successfully completing the course and passing the necessary NASSCOM Assessment, the learner is eligible for Government of India (GOI) incentives and will be issued a NASSCOM certification.

Why Future Skills Prime?

The first-of-its-kind collaboration between government and industry to build a nationwide skilling ecosystem for digital technologies.
Complete skill development, from evaluation to certification.
Content that is affordable and credible, vetted by industry leaders.
Learn faster with bite-sized course units.
Industry-recognized certifications

Who all are eligible for the GoI incentive?

All Indian nationals over the age of 18 can apply for the GoI Incentive. The current plan includes beneficiaries from the following general categories:

IT workers in IT and non-IT organisations
Non-IT employees that want to leverage new and emerging technology in their fields
Employees whose talents for a specific job have become obsolete.
Employees of the Central and State Governments, as well as employees of PSUs and autonomous organisations (Govt. Employees)
Fresh Recruits who have not yet started working, as well as those undergoing/selected for internship and apprenticeship roles in IT/ITeS

What is Future Skills Prime?

What do I need to do to unlock CertHippo Data Science with Python Training certificate?

To obtain the CertHippo Data Science with Python Training course completion certificate, you must fulfil the following requirements:

Participate fully in this CertHipoo Data Science with Python Training Course.
Evaluation and completion of the mentioned quizzes and tasks.

Is Data Scientist a good career option?

Yes, Data Scientist is a viable career path for those who enjoy working with data and gaining insights from it. With the explosion of data in recent years, the demand for skilled data scientists has skyrocketed. As a Data Scientist, you can work in a range of areas, including healthcare, finance, and marketing. A strong foundation in statistics, machine learning, and programming skills, as well as a good understanding of business and domain expertise, are often required for the job. A Data Scientist is in charge of gathering, analysing, and interpreting massive and complicated data sets in order to guide company choices and strategies. Overall, data science is a tough and rewarding career path with a bright future.

Is Machine Learning Engineer a good career option?

Yes, Machine Learning Engineer is a viable career path for those who want to work with machine learning algorithms and put them into real-world applications. Machine learning is a fast expanding subject with a growing demand for people who can create and deploy machine learning models to automate tasks and extract insights from massive volumes of data. As a Machine Learning Engineer, you can work in a range of industries, including healthcare, banking, and e-commerce. A strong foundation in machine learning, programming abilities, and a decent understanding of software engineering principles are often required for the job. Overall, machine learning engineering can be a hard and rewarding professional path with a bright future.

How can beginners learn Data Science and Machine Learning?

To learn data science and machine learning as a novice, begin with Python programming and then on to data analysis. After mastering data analysis, one can move on to grasp the fundamentals of machine learning and apply machine learning algorithms to real-world issues. CertHippo Data Science with Python Certification Training is an organised learning experience that assists novices in gaining practical experience and developing the abilities required to become effective in data science and machine learning.

What is the value of Data Science with Python Certification?

Data Science with Python Certification teaches data science, machine learning, and Python programming. This certification is useful for a number of reasons:

Mastery of Key Skills: Certification demonstrates that a person has a good understanding of data science ideas, machine learning methodologies, and Python programming skills.
Improves Job Prospects: Data science and machine learning are fast-growing businesses, and certification can help you get a better job by demonstrating your skills in these fields.
Increases Earning Potential: Certified data scientists and machine learning engineers frequently earn better pay than non-certified colleagues.
Enhances Credibility: Certification is a recognized mark of knowledge that can boost a person's credibility in their area.
Maintains Skills: Data science and machine learning are topics that are always growing, and certification demands employees to stay up to date with the latest technology and methodologies.
Enables Career Advancement: Certification allows individuals to develop their careers by proving mastery of important skills and enhancing their value to their organization.

Can I enroll in Data Science with Python Certification Training if I do not have any coding background?

This Data Science with Python course does not require any prior coding knowledge. The course begins with core modules that cover the principles of Python coding. In fact, no prior expertise of data science or machine learning is required. This course includes all necessary topics from the ground up.

Online Training FAQs

What is Python for Data Science?

Data is all around us, and data science will help us extract it. Python may be used to implement numerous data science applications. Python is a general-purpose programming language that may be used to create websites, backend APIs, and scripting. Python's built-in libraries, frameworks, and tools may be used to execute a variety of data science activities.

Why should I learn Data Science with a Python Course?

Python is unquestionably one of the most popular data science languages, capable of data analysis, manipulation, and visualization. It offers access to a large number of Data Science libraries, making it ideal for designing applications and applying algorithms.

Where Can I Learn Python for Data Science?

Although there are many free learning materials accessible, it is essential that you choose one that teaches data science extremely effectively. You should select a platform that will teach you interactively and includes a curriculum that will guide you through your data science journey. CertHippo is one such platform, as we provide the best online Data Science with Python course for data science, taking you from novice to data analyst or data scientist in Python.

Can I learn Python for Data Science online?

Online learning has become easier and more efficient as a result of technological advancements. It allows you to learn at your own speed with no restrictions. CertHippo Data Science with Python certification course includes live courses as well as online access to study materials from any place and at any time. With our wide and expanding library of lessons, articles, and YouTube videos, you will be able to comprehend the important ideas fast. We provide a 24-hour support service to address any queries you may have after your class has concluded.

Who are the instructors for the Python Data Science Course?

CertHippo! instructors are all industry practitioners with at least 10-12 years of relevant IT experience. These are subject matter experts who have been educated by CertHippo to provide an excellent learning experience to Python Data Science Training attendees.

What is Data Science with Python Course duration?

Learning basic Python programming principles, such as object-oriented programming and basic Python syntax, might take between five and ten weeks in a Data Science with Python Course. It is crucial to remember that the amount of time it takes to learn Python programming is dependent on your previous expertise in web development, data science, and other relevant topics.

How will I execute practical’s in CertHippo Data Science with Python Certification Course?

You will complete your Assignments/Case Studies using Jupyter Notebook, which is already installed on your Cloud Lab environment and whose login information is available on your LMS. You will be using a browser to access your Cloud Lab environment. If you have any questions, the 24 hour support crew will respond quickly.

Course Description

Why Learn Data Science with Python?

Python is a premier, flexible, and strong open-source language that is simple to learn and use, with extensive modules for data processing and analysis. It has been used in scientific computing and highly quantitative sectors such as finance, oil and gas, physics, and signal processing for over a decade. It has remained a popular choice among data scientists who use it to construct and run Machine Learning applications and other scientific calculations. Python’s concise syntax and quick compilation feature decreased development time in half. Python’s built-in debugger makes debugging applications a snap.It has emerged as the most popular Language for Data Analytics, and rising search patterns show that it is the Next Big Thing and a requirement for Data Analytics Professionals.

What are the objectives of our Data Science with Python Training Course?

After completing this Data Science using Python Certification course, you will be able to:

Programmatically download and analyze data
Learn techniques to deal with different types of data – ordinal, categorical, encoding
Learn data visualization
Using I python notebooks, master the art of presenting step by step data analysis
Gain insight into the ‘Roles’ played by a Machine Learning Engineer
Describe Machine Learning
Work with real-time data
Learn tools and techniques for predictive modeling
Discuss Machine Learning algorithms and their implementation
Validate Machine Learning algorithms
Perform Text Mining and Sentiment analysis
Explain Time Series and its related concepts
Gain expertise to handle business in future, living the present

EduCerts offers the best online course for Python Data Science. Enroll now with our data Science with Python training and get a chance to learn from industrial giants.

Who should go for this Python Data Science online course?

EduCerts’s course is a good fit for the below professionals:

Programmers, Developers, Technical Leads, Architects
Developers aspiring to be a ‘Machine Learning Engineer’
Analytics Managers who are leading a team of analysts
Business Analysts who want to understand Machine
Learning (ML) Techniques
Information Architects who want to gain expertise in
Predictive Analytics
Professionals who want to design automatic predictive models

What are the prerequisites for this Data Science with Python Course?

The fundamental grasp of Computer Programming Languages is required for the EduCerts Python Data Science course study. Foundations of Data Analysis performed using data analysis programmers such as SAS/R will be advantageous. Nevertheless, you will be supplied with gratis “Python Statistics for Data Science” as a self-paced course after you enroll for the Data Science with Python certification course.

What is Data Science with Python syllabus?

The Data Science with Python Course syllabus covers:

Introduction to Python
Sequences and File Operations
Deep Dive – Functions, OOPs, Modules, Errors and Exceptions
Introduction to NumPy, Pandas and Matplotlib
Data Manipulation
Introduction to Machine Learning with Python
Supervised Learning – I
Dimensionality Reduction
Supervised Learning – II
Unsupervised Learning
Association Rules Mining and Recommendation Systems
Reinforcement Learning
Time Series Analysis
Model Selection and Boosting
Statistical Foundations (Self-Paced)
Data Connection and Visualization in Tableau (Self-paced)
Advanced Visualizations (Self-paced)