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.
- 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.
|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|
|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|
|Deep Dive – Functions, OOPs, Modules, Errors and Exceptions|
|Variable Scope and Returning Values||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|
|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|
|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|
|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|
|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|
|Introduction to Dimensionality||00:00:00|
|Why Dimensionality Reduction||00:00:00|
|Scaling dimensional model||00:00:00|
|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|
|Grid Search vs Random Search||00:00:00|
|Implementation of Support Vector Machine for Classification||00:00:00|
|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|
|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|
|How does Recommendation Engines work?||00:00:00|
|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|
|Time Series Analysis|
|What is Time Series Analysis?||00:00:00|
|Importance of TSA||00:00:00|
|Components of TSA||00:00:00|
|ACF & PACF||00:00:00|
|Model Selection and Boosting|
|What is Model Selection?||00:00:00|
|The need for Model Selection||00:00:00|
|What is Boosting?||00:00:00|
|How Boosting Algorithms work?||00:00:00|
|Types of Boosting Algorithms||00:00:00|
|Which case studies will be a part of this Python Certification Course?|
|PYTHON CERTIFICATION COURSE PROCESS|
No Reviews found for this course.