Python Certification Training for Data Science

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Certs Learning
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Data Science

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.
Certs Learning’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.
Certs Learning’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.

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
  • 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
  • Explain Time Series and its related concepts
  • Perform Text Mining and Sentimental analysis
  • Gain expertise to handle business in future, living the present

Who should go for this Python Data Science Certification Course?

Certs Learning’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

What are the prerequisites for this Python Course?

The pre-requisites for Certs Learning’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.

1
Introduction to Python

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
2
Sequences and File Operations

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
3
Deep Dive – Functions, OOPs, Modules, Errors and Exceptions

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
4
Introduction to NumPy, Pandas and Matplotlib

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


5
Data Manipulation

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
6
Introduction to Machine Learning with Python

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
7
Supervised Learning – I

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
8
Dimensionality Reduction

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
9
Supervised Learning – II

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
10
Unsupervised Learning

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
11
Association Rules Mining and Recommendation Systems

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
12
Reinforcement Learning

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
13
Time Series Analysis

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
14
Model Selection and Boosting

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
Certs Learning’s Python Certification training will help you in getting certified as a Python developer and get relevant jobs in this field. Our instructor led sessions combined with industry relevant course materials will make sure that your knowledge is up to date.
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Python is one of the programming languages that are in great demand in the job market. With its ease of use and simplicity, Python is being widely implemented in various industries. Learning Python will help open up a lot of opportunities for developers looking for a change or anyone looking to get started with a career in IT.
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Lectures: 14