Python Certification Training

Python Certification Training

OL Tech Edu's Python programming certification course enables you to learn Python from scratch. This Python Course will also help you master important Python programming concepts such as data operations, file operations, object-oriented programming and various Python libraries such as Pandas, Numpy, Matplotlib which are essential for Data Science. OL Tech Edu's Python Certification Training course is also a gateway towards your Data Science career.

Python is the preferred language for new technologies such as Data Science and Machine Learning.

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

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Course Curriculum

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Python Certification Training for Data Science

SELF PACED

OL Tech Edu's Python programming certification course enables you to learn Python from scratch. This Python Course will also help you master important Python programming concepts such as data operations, file operations, object-oriented programming and various Python libraries such as Pandas, Numpy, Matplotlib which are essential for Data Science. OL Tech Edu's Python Certification Training course is also a gateway towards your Data Science career.

  • WEEK 5-6
  • 10 Modules
  • 6 Hours
Python Certification Training for Data Science
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 and 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 TSA.
  • Components of TSA.
  • White Noise.
  • AR Model.
  • MA Model.
  • ARMA Model.
  • ARIMA Model.
  • Stationarity.
  • ACF & PACF.

Hands on/Demo:
  • Checking Stationarity.
  • Converting a Non-Stationary data to Stationary.
  • Implementing Dickey-Fuller Test.
  • Plot ACF and PACF.
  • Generating the ARIMA Plot.
  • TSA 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.

Program Syllabus

Curriculum

You can also view the program syllabus by downloading this program Curriculum.

Curriculum

Projects

How will I execute practical’s in OL Tech Edu s Python Certification Course?

You will do your Assignments/Case Studies using Jupyter Notebook that is already installed on your Cloud Lab environment whose access details will be available on your LMS. You will be accessing your Cloud Lab environment from a browser. For any doubt, the 24*7 support team will promptly assist you.

How will I execute the practicals?

For executing the practicals, participants will be enabled to create a life-time free SFDC development environment on the Cloud. There are few simple tools that need to installed for specific topics which comprise around 10% of the total course and will be provided in that module.

Which case studies will be a part of this Python Certification Course?

Maple Leaves Ltd is a start-up company which makes herbs from different types of plants and its leaves.Currently, the system they use to classify the trees which they import in a batch is quite manual.

Course Description

About The Course
About the Python Certification 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.
  • OL Tech Edu'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.
  • OL Tech Edu’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.
Objectives Of Our Online PySpark Training Course
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.
Go For Online Spark Training
What are the objectives of our Python Certification Course? After completing this Data Science Certification training, you will be able to:
  • Programmatically download and analyze data.
  • Learn techniques to deal with different types of data – ordinal, categorical, encoding.
  • 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.
  • Validate Machine Learning algorithms.
  • Explain Time Series and its related concepts.
  • Perform Text Mining and Sentimental analysis.
  • Gain expertise to handle business in future, living the present.
Learning With Our PySpark Certification Training
Who should go for this Python Data Science Certification Course? 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.
Who Should Go For Our PySpark Training Course
What are the pre-requisites for this Python Course?
  • The pre-requisites for OL Tech Edu'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.

Course Certification

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Features

Explore step by step paths to get started on your journey to Jobs of Today and Tomorrow.

Instructor-led Sessions

30 Hours of Online Live Instructor-Led Classes.
Weekend Class : 10 sessions of 3 hours each.

Real Life Case Studies

Real-life Case Studies

Live project based on any of the selected use cases, involving implementation of the various real life solutions / services.

Assignments

Assignments

Each class will be followed by practical assignments.

24 x 7 Expert Support

24 x 7 Expert Support

We have 24x7 online support team to resolve all your technical queries, through ticket based tracking system, for the lifetime.

Certification

Certification

Towards the end of the course, OL Tech Edu certifies you for the course you had enrolled for based on the project you submit.

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