Certybox Offers Comprehensive And yet Affordable Program In Python. The Program Has An Employment-Oriented Approach And Is Based On A Detailed Research Of Companies’ Requirements. It Prepares Students For Roles Like Business Analyst, Data Analyst And Data Scientist And Is Available In Online And Offline Modes.

Certybox Offers A Comprehensive And Yet Affordable Program In Python. The Program Has An Employment-Oriented Approach And Is Based On A Detailed Research Of Companies’ Requirements. It Prepares Students For Roles Like Business Analyst, Data Analyst And Data Scientist And Is Available In Online And Offline Modes.

About the Course

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.

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.

Our 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.

Why Learn Python?

It’s continued to be a favorite 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, and 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.

Below are the major features and applications due to which people choose Python as their first programming language:

  • Python’s popularity & high salary
  • Python is used in Data Science
  • Python’s scripting & automation
  • Python used with Big Data
  • Python supports Testing
  • Computer Graphics in Python
  • Python used in Artificial Intelligence
  • Python in Web Development
  • Python is portable & extensible
  • Python is simple & easy to learn

What are the objectives of our Python Certification Course?

After completing this 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?

This 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 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.

MAJOR AREAS EMPLOYING PYTHON PROFESSIONALS

  • FINANCIAL ANALYTICS- Credit Scoring, Risk based pricing, Fraud Detection and Prediction, Recovery Management, Loss Forecasting, Risk Profiling, Portfolio Stress Testing.
  • MARKETING- Segmentation, Marketing Mix Optimization, Competitor Analysis, Channel Analysis, Sales Performance Analysis, Campaign Analysis, Sales Pipeline Reporting.
  • RETAIL- Customer Analytics, Merchandizing Analytics, Store Operations, Inventory Analytics, Market Spend Optimization.
  • CUSTOMER ANALYTICS- Loyalty Analytics, Customer Life Time Value, Propensity Analytics, Churn Analytics, Customer Segmentation, Cross- Sell or Up sell Models.
  • WEB ANALYTICS- Click Analytics, Customer Life cycle Analytics, Social Media Analytics, Sentiment Analytics, Online Traffic Analytics, and Conversion Analytics.
  • HUMAN RESOURCES- Recruitment Analytics, Compensation Analytics, Talent Analytics, Training Analytics, Retention Analytics, Workforce Analytics.

Deliverables

  • Real-life Case Studies- Live project based on any of the selected use cases, involving the implementation of Data Science.
  • Coverage- Comprehensive coverage on various analytical tools.
  • Advanced Analytics: Learn Text Analytics, Machine Learning, Marketing Analytics and Retail Analytics
  • Online Material: 24*7 accesses to practice material, videos, quizzes, mock tests, etc., to ensure learning efficiency.
  • 24 x 7 Expert Support- Expert faculty with wide industry experience in the Analytics industry and alumni of top universities
  • Mentoring: Get mentoring from data scientist working in leading companies such as Mckinsey, Deloitte, Mu Sigma, Google, PWC etc.
  • Lifetime Access- You gets lifetime access to the Learning Management System (LMS). Class recordings and presentations can be viewed online from the LMS.
  • Placement assistance: Candidates will receive 100% placement assistance which includes interview grooming, resume writing etc.

Course Curriculum

Introduction to Python
The Companies using Python 00:00:00
Discuss Python Scripts on UNIX/Windows 00:00:00
Values, Types, Variables 00:00:00
Sequences and File Operations
Python files I/O Functions 00:00:00
Numbers 00:00:00
Strings and related operations 00:00:00
Lists and related operations 00:00:00
Dictionaries and related operations 00:00:00
Sets and related operations 00:00:00
Hands On/Demo 00:00:00
Deep Dive – Functions, OOPs, Modules, Errors and Exceptions
Functions 00:00:00
Function Parameters 00:00:00
Global Variables 00:00:00
Variable Scope and Returning Values 00:00:00
Lambda Functions 00:00:00
Object-Oriented Concepts 00:00:00
Standard Libraries 00:00:00
Modules Used in Python 00:00:00
The Import Statements 00:00:00
Module Search Path 00:00:00
Errors and Exception Handling 00:00:00
Handling Multiple Exceptions 00:00:00
Hands On/Demo 00:00:00
Introduction to NumPy, Pandas and Matplotlib
Indexing slicing and iterating 00:00:00
Pandas – data structures & index operations 00:00:00
Reading and Writing data from Excel/CSV formats into Pandas 00:00:00
Matplotlib library 00:00:00
Grids, axes, plots 00:00:00
Markers, colours, fonts and styling 00:00:00
Types of plots – bar graphs, pie charts, histograms 00:00:00
Contour plots 00:00:00
Hands On/Demo 00:00:00
Data Manipulation
Basic Functionalities of a data object 00:00:00
Merging of Data objects 00:00:00
Merging of Data objects 00:00:00
Concatenation of data objects 00:00:00
Types of Joins on data objects 00:00:00
Exploring a Dataset 00:00:00
Analysing a dataset 00:00:00
Hands On/Demo 00:00:00
Introduction to Machine Learning with Python
Python Revision (numpy, Pandas, scikit learn, matplotlib) 00:00:00
What is Machine Learning? 00:00:00
Machine Learning Use-Cases 00:00:00
Machine Learning Process Flow 00:00:00
Machine Learning Categories 00:00:00
Linear regression 00:00:00
Gradient descent 00:00:00
Hands On/Demo 00:00:00
Dimensionality Reduction
Introduction to Dimensionality 00:00:00
Why Dimensionality Reduction 00:00:00
PCA 00:00:00
Factor Analysis 00:00:00
Scaling dimensional model 00:00:00
LDA 00:00:00
Hands-On/Demo 00:00:00
Supervised Learning
What is Naïve Bayes? 00:00:00
How Naïve Bayes works? 00:00:00
Implementing Naïve Bayes Classifier 00:00:00
What is Support Vector Machine? 00:00:00
Illustrate how Support Vector Machine works? 00:00:00
Hyperparameter Optimization 00:00:00
Grid Search vs Random Search 00:00:00
Implementation of Support Vector Machine for Classification 00:00:00
Hands-On/Demo 00:00:00
Unsupervised Learning
What is Clustering & its Use Cases? 00:00:00
What is K-means Clustering? 00:00:00
How does K-means algorithm work? 00:00:00
How to do optimal clustering 00:00:00
What is C-means Clustering? 00:00:00
What is Hierarchical Clustering? 00:00:00
How Hierarchical Clustering works? 00:00:00
Hands-On/Demo 00:00:00
Association Rules Mining and Recommendation Systems
What are Association Rules? 00:00:00
Association Rule Parameters 00:00:00
Calculating Association Rule Parameters 00:00:00
Recommendation Engines 00:00:00
How does Recommendation Engines work? 00:00:00
Collaborative Filtering 00:00:00
Content-Based Filtering 00:00:00
Hands-On/Demo 00:00:00
Reinforcement Learning
What is Reinforcement Learning 00:00:00
Why Reinforcement Learning 00:00:00
Elements of Reinforcement Learning 00:00:00
Exploration vs Exploitation dilemma 00:00:00
Epsilon Greedy Algorithm 00:00:00
Markov Decision Process (MDP) 00:00:00
Q values and V values 00:00:00
Q – Learning 00:00:00
α values 00:00:00
Hands-On/Demo 00:00:00
Time Series Analysis
What is Time Series Analysis? 00:00:00
Importance of TSA 00:00:00
Components of TSA 00:00:00
White Noise 00:00:00
AR model 00:00:00
MA model 00:00:00
ARMA model 00:00:00
ARIMA model 00:00:00
Stationarity 00:00:00
ACF & PACF 00:00:00
Hands on/Demo 00:00:00
Model Selection and Boosting
What is Model Selection? 00:00:00
The need for Model Selection 00:00:00
Cross-Validation 00:00:00
What is Boosting? 00:00:00
How Boosting Algorithms work? 00:00:00
Types of Boosting Algorithms 00:00:00
Adaptive Boosting 00:00:00
Hands on/Demo 00:00:00
Which case studies will be a part of this Python Certification Course?
Case Studies:1-5 00:00:00
PYTHON CERTIFICATION COURSE PROCESS

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