Python Certification Training for Data Science

According to the TIOBE index, Python is one of the most popular programming languages in the world.

Python Certification Training for Data Science

Course Description

Edureka's Python course helps you gain expertise in Quantitative Analysis, data mining, and the presentation of data to see beyond the numbers by transforming your career into Data Scientist role. You will use libraries like Pandas, Numpy, Matplotlib, Scikit and master the concepts like Python Machine Learning Algorithms such as Regression, Clustering, Decision Trees, Random Forest, Naïve Bayes and Q-Learning and Time Series. Throughout the Course, you’ll be solving real-life case studies on Media, Healthcare, Social Media, Aviation, HR and so on. About the Python Course Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. For over a decade, Python has been used in scientific computing and highly q... Read More »

Edureka’s Python course helps you gain expertise in Quantitative Analysis, data mining, and the presentation of data to see beyond the numbers by transforming your career into Data Scientist role. You will use libraries like Pandas, Numpy, Matplotlib, Scikit and master the concepts like Python Machine Learning Algorithms such as Regression, Clustering, Decision Trees, Random Forest, Naïve Bayes and Q-Learning and Time Series. Throughout the Course, you’ll be solving real-life case studies on Media, Healthcare, Social Media, Aviation, HR and so on.

About the Python Course

Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. For over a decade, Python has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing.

Edureka’s Python Certification Training not only focuses on fundamentals of Python, Statistics and Machine Learning but also helps one gain expertise in applied Data Science at scale using Python. The training is a step by step guide to Python and Data Science with extensive hands on. The course is packed with several activity problems and assignments and scenarios that help you gain practical experience in addressing predictive modeling problem that would either require Machine Learning using Python. Starting from basics of Statistics such as mean, median and mode to exploring features such as Data Analysis, Regression, Classification, Clustering, Naive Bayes, Cross Validation, Label Encoding, Random Forests, Decision Trees and Support Vector Machines with a supporting example and exercise help you get into the weeds.

Furthermore, you will be taught of Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to train your machine based on real-life scenarios using Machine Learning Algorithms.

Edureka’s Python course will also cover both basic and advanced concepts of Python like writing Python scripts, sequence and file operations in Python. You will use libraries like pandas, numpy, matplotlib, scikit, and master the concepts like Python machine learning, scripts, and sequence.

Why Learn Python

It’s continued to be a favourite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging programs is a breeze in Python with its built in debugger.

It runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.

It has evolved as the most preferred Language for Data Analytics and the increasing search trends on Python also indicates that it is the ” Next Big Thing ” and a must for Professionals in the Data Analytics domain.


Features

Real Life Case Studies: Live project based on any of the selected use cases, involving implementation of Data Science with Python.

Assignments: Every class will be followed by practical assignments which aggregates to minimum 60 hours.

24×7 Expert Support: Lifetime access to our 24×7 online support team who will resolve all your technical queries, through ticket based tracking system.

Forum: Access to the global community forum for all our users that further facilitates learning through peer interaction and knowledge sharing.

NEW! Cloud Lab: Cloud Lab has been provided to ensure you get real-time hands-on experience to practice your new skills on a pre-configured environment.

Read Less
Course Outcomes:
  • 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
Course Details:

Target Audience

Edureka’s Data Science certification course in Python 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
  • 'Python' professionals who want to design automatic predictive models

Access Timeframe

Lifetime access to Learning Management System (LMS) which has class presentations, quizzes, installation guide & class recordings.

Prerequisites

The pre-requisites for edureka's Python course include the basic understanding of Computer Programming Languages. Fundamentals of Data Analysis practiced over any of the data analysis tools like SAS/R will be a plus. However, you will be provided with complimentary “Python Statistics for Data Science” as a self-paced course once you enroll for the course.
Certificate Info:

Type of Certification

Python for Data Science Professional Certificate of Course Completion.

Format of Certification

Digital

Professional Association/Affiliation

edureka certification has industry recognition and we are the preferred training partner for many MNCs e.g.Cisco, Ford, Mphasis, Nokia, Wipro, Accenture, IBM, Philips, Citi, Ford, Mindtree, BNYMellon and more.

Method of Obtaining Certification

Towards the end of the course, you will be working on a project. Edureka certifies you as a Data Scientist with proficiency in Python.
About Instructor:

All the instructors at edureka! are practitioners from the Industry with minimum 10-12 yrs of relevant IT experience. They are subject matter experts and are trained by edureka for providing an awesome learning experience to the participants.


Course Outline

Learning Objectives:
  • You will get a brief idea of what Python is and touch on the basics.
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/Demo:
  • Creating “Hello World” code
  • Variables
  • Demonstrating Conditional Statements
  • Demonstrating Loops
Skills:
  • Fundamentals of Python programming
Learning Objectives:
  • Learn different types of sequence structures, related operations and their usage. Also learn diverse ways of opening, reading, and writing to files.
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/Demo:
  • Tuple - properties, related operations, compared with a list
  • List - properties, related operations
  • Dictionary - properties, related operations
  • Set - properties, related operations
Skills:
  • File Operations using Python
  • Working with data types of Python
Learning Objectives:
  • In this Module, you will learn how to create generic python scripts, how to address errors/exceptions in code and finally how to extract/filter content using regex.
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/Demo:
  • 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:
  • Error and Exception management in Python
  • Working with functions in Python
Learning Objectives:
  • This Module helps you get familiar with basics of statistics, different types of measures and probability distributions, and the supporting libraries in Python that assist in these operations. Also, you will learn in detail about data visualization.
Topics:
  • 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
  • matplotlib library
  • Grids, axes, plots
  • Markers, colours, 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 dataframes, Importing and exporting data
  • Matplotlib - Using Scatterplot, histogram, bar graph, pie chart to show information, Styling of Plot
Skills:
  • Probability Distributions in Python
  • Python for Data Visualization
Learning Objective:
  • Through this Module, you will understand in detail about 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
  • Analysing a dataset
Hands On/Demo:
  • Pandas Function- Ndim(), axes(), values(), head(), tail(), sum(), std(), iteritems(), iterrows(), itertuples()
  • GroupBy operations
  • Aggregation
  • Concatenation
  • Merging
  • Joining
Skills:
  • Python in Data Manipulation
Learning Objectives:
  • In this module, you will learn the concept of Machine Learning and its types.
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
  • Gradient descent
Hands On/Demo:
  • Linear Regression – Boston Dataset
Skills:
  • Machine Learning concepts
  • Machine Learning types
  • Linear Regression Implementation
Learning Objectives:
  • In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.
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
Skills:
  • Supervised Learning concepts
  • Implementing different types of Supervised Learning algorithms
  • Evaluating model output
Learning Objectives:
  • In this module, you will learn about the impact of dimensions within data. You will be taught to perform factor analysis using PCA and compress dimensions. Also, you will be developing LDA model.
Topics:
  • Introduction to Dimensionality
  • Why Dimensionality Reduction
  • PCA
  • Factor Analysis
  • Scaling dimensional model
  • LDA
Hands-On/Demo:
  • PCA
  • Scaling
Skills:
  • Implementing Dimensionality Reduction Technique
Learning Objectives:
  • In this module, you will learn Supervised Learning Techniques and their implementation, for example, Decision Trees, Random Forest Classifier etc.
Topics:
  • What is Naïve Bayes?
  • How Naïve Bayes works?
  • Implementing Naïve Bayes Classifier
  • What is 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
Skills:
  • Supervised Learning concepts
  • Implementing different types of Supervised Learning algorithms
  • Evaluating model output
Learning Objectives:
  • In this module, you will learn about Unsupervised Learning and the various types of clustering that can be used to analyze the data.
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
Skills:
  • Unsupervised Learning
  • Implementation of Clustering – various types
Learning Objectives:
  • In this module, you will learn Association rules and their extension towards recommendation engines with Apriori algorithm.
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:
  • Apriori Algorithm
  • Market Basket Analysis
Skills:
  • Data Mining using python
  • Recommender Systems using python
Learning Objectives:
  • In this module, you will learn about developing a smart learning algorithm such that the learning becomes more and more accurate as time passes by. You will be able to define an optimal solution for an agent based on agent-environment interaction.
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
  • α values
Hands-On/Demo:
  • 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
Learning Objectives:
  • In this module, you will learn about Time Series Analysis to forecast dependent variables based on time. You will be taught different models for time series modeling such that you analyze a real time-dependent data for forecasting.
Topics:
  • What is Time Series Analysis?
  • Importance of TSAComponents of TSAWhite NoiseAR modelMA modelARMA modelARIMA modelStationarityACF & PACF
Hands on/Demo:
  • Checking Stationarity
  • Converting a non-stationary data to stationaryImplementing Dickey-Fuller TestPlot ACF and PACFGenerating the ARIMA plotTSA Forecasting
Skills:
  • TSA in Python
Learning Objectives:
  • In this module, you will learn about selecting one model over another. Also, you will learn about Boosting and its importance in Machine Learning. You will learn on how to convert weaker algorithms into stronger ones.
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:
  • Cross-Validation
  • AdaBoost
Skills:
  • Model Selection
  • Boosting algorithm using python

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