Python For Data Science Certification

Python programming enables one to become a master in data analytics, machine learning, and web scraping. python has created quite a few tools for efficient working. the outcome of this course is to become an oracle in various packages of python like Numpy, Scipy, and Scikit- learn for performing data analysis, implementing machine learning models, and NLP. the course is inclusive of 2 echt and real-life industry projects and jupyter notebook labs provide an interactive hands-on experience. it is the best track for both beginners and experienced professionals to overtake the world.

key features:
  1. 40 hours of instructor-led training.
  2. 24 hours of self paced learning videos
  3. 4 echt and reallife industry based projectsin the doamin of telecom, stock market,etc,.
  4. Interactive learning environment by jupiter notebooks lab.
  5. developing sound knowledge on web scraping.
  6. fortunately getting free python basic course.

COURSE CONTENT:

00.Course Overview
  • 0.1Course Overview
01 - Data Science Overview
  • 1.1 Introduction to Data Science
  • 1.2 Different Sectors Using Data Science
  • 1.3 Purpose and Components of Python
  • 1.4 Quiz
  • 1.5 Key Takeaways
02 - Data Analytics Overview
  • 2.1 Data Analytics Process
  • 2.2 Knowledge Check
  • 2.3 Exploratory Data Analysis(EDA)
  • 2.4 EDA-Quantitative Technique
  • 2.5 EDA - Graphical Technique
  • 2.6 Data Analytics Conclusion or Predictions
  • 2.7 Data Analytics Communication
  • 2.8 Data Types for Plotting
  • 2.9 Data Types and Plotting
  • 2.10 Knowledge Check
  • 2.11 Quiz
  • 2.12 Key Takeaways
03 - Statistical Analysis and Business Applications
  • 3.1 Introduction to Statistics
  • 3.2 Statistical and Non-statistical Analysis
  • 3.3 Major Categories of Statistics
  • 3.4 Statistical Analysis Considerations
  • 3.5 Population and Sample
  • 3.6 Statistical Analysis Process
  • 3.7 Data Distribution
  • 3.8 Dispersion
  • 3.9 Knowledge Check
  • 3.10 Histogram
  • 3.11 Knowledge Check
  • 3.12 Testing
  • 3.13 Knowledge Check
  • 3.14 Correlation and Inferential Statistics
  • 3.15 Quiz
  • 3.16 Key Takeaways
04 - Python Environment Setup and Essentials
  • 4.1 Anaconda
  • 4.2 Installation of Anaconda Python Distribution (contd.)
  • 4.3 Data Types with Python
  • 4.4 Basic Operators and Functions
  • 4.5 Quiz
  • 4.6 Key Takeaways
05 - Mathematical Computing with Python (NumPy)
  • 5.1 Introduction to Numpy
  • 5.2 Activity-Sequence it Right
  • 5.3 Demo 01-Creating and Printing an ndarray
  • 5.4 Knowledge Check
  • 5.5 Class and Attributes of ndarray
  • 5.6 Basic Operations
  • 5.7 Activity-Slice It
  • 5.8 Copy and Views
  • 5.9 Mathematical Functions of Numpy
  • 5.10 Assignment 01
  • 5.10 Assignment 01
  • 5.11 Assignment 01 Demo
  • 5.12 Assignment 02
  • 5.14 Quiz
  • 5.15 Key Takeaways
06 - Scientific computing with Python (Scipy)
  • 6.1 Introduction to SciPy
  • 6.2 SciPy Sub Package - Integration and Optimization
  • 6.3 Knowledge Check
  • 6.4 SciPy sub package
  • 6.5 Demo - Calculate Eigenvalues and Eigenvector
  • 6.6 Knowledge Check
  • 6.7 SciPy Sub Package - Statistics, Weave and IO
  • 6.8 Assignment 01
  • 6.9 Assignment 01 Demo
  • 6.10 Assignment 02
  • 6.11 Assignment 02 Demo
  • 6.12 Quiz
  • 6.13 Key Takeaways
07 - Data Manipulation with Pandas
  • 7.1 Introduction to Pandas
  • 7.2 Knowledge Check
  • 7.3 Understanding DataFrame
  • 7.4 View and Select Data Demo
  • 7.5 Missing Values
  • 7.6 Data Operations
  • 7.7 Knowledge Check
  • 7.8 File Read and Write Support
  • 7.9 Knowledge Check-Sequence it Right
  • 7.10 Pandas Sql Operation
  • 7.11 Assignment 01
  • 7.12 Assignment 01 Demo
  • 7.13 Assignment 02
  • 7.14 Assignment 02 Demo
  • 7.15 Quiz
  • 7.16 Key Takeaways
08 - Machine Learning with Scikit–Learn
  • 8.1 Machine Learning Approach
  • 8.2 Steps 1 and 2
  • 8.3 Steps 3 and 4
  • 8.4 How it Works
  • 8.5 Steps 5 and 6
  • 8.6 Supervised Learning Model Considerations
  • 8.7 Knowledge Check
  • 8.8 Scikit-Learn
  • 8.9 Knowledge Check
  • 8.10 Supervised Learning Models - Linear Regression
  • 8.11 Supervised Learning Models - Logistic Regression
  • 8.12 Unsupervised Learning Models
  • 8.13 Pipeline
  • 8.14 Model Persistence and Evaluation
  • 8.15 Knowledge Check
  • 8.16 Assignment 01
  • 8.17 Assignment 010
  • 8.18 Assignment 02
  • 8.19 Assignment 02
  • 8.20 Quiz
  • 8.21 Key Takeaways
09 - Natural Language Processing with Scikit Learn
  • 9.1 NLP Overview
  • 9.2 NLP Applications
  • 9.3 Knowledge check
  • 9.4 NLP Libraries-Scikit
  • 9.5 Extraction Considerations
  • 9.6 Scikit Learn-Model Training and Grid Search
  • 9.7 Assignment 01
  • 9.8 Demo Assignment 01
  • 9.9 Assignment 02
  • 9.10 Demo Assignment 02
  • 9.11 Quiz
  • 9.12 Key Takeaway
10 - Data Visualization in Python using matplotlib
  • 10.1 Introduction to Data Visualization
  • 10.2 Knowledge Check
  • 10.3 Line Properties
  • 10.4 (x,y) Plot and Subplots
  • 10.5 Knowledge Check
  • 10.6 Types of Plots
  • 10.7 Assignment 01
  • 10.8 Assignment 01 Demo
  • 10.9 Assignment 02
  • 10.10 Assignment 02 Demo
  • 10.11 Quiz
  • 10.12 Key Takeaways
11 - Web Scraping with BeautifulSoup
  • 11.1 Web Scraping and Parsing
  • 11.2 Knowledge Check
  • 11.3 Understanding and Searching the Tree
  • 11.4 Navigating options
  • 11.5 Demo3 Navigating a Tree
  • 11.6 Knowledge Check
  • 11.7 Modifying the Tree
  • 11.8 Parsing and Printing the Document
  • 11.9 Assignment
  • 11.10 Assignment 01 Demo
  • 11.11 Assignment
  • 11.12 Assignment 02 demo
  • 11.13 Quiz
  • 11.14 Key takeaways
12 - Python integration with Hadoop MapReduce and Spark
  • 12.1 Why Big Data Solutions are Provided for Python
  • 12.2 Hadoop Core Components
  • 12.3 Python Integration with HDFS using Hadoop Streaming
  • 12.4 Demo 01 - Using Hadoop Streaming for Calculating Word Count
  • 12.5 Knowledge Check
  • 12.6 Python Integration with Spark using PySpark
  • 12.7 Demo 02 - Using PySpark to Determine Word Count
  • 12.8 Knowledge Check
  • 12.9 Assignment 01
  • 12.10 Assignment 01 Demo
  • 12.11 Assignment 02
  • 12.12 Assignment 02 Demo
  • 12.13 Quiz
  • 12.14 Key takeaways

On what this course focus?

The data science with python is created to impart adequate skills on various libraries and packages required to perform data analysis, data visualization, web scraping, machine learning, and natural language processing using python. this is a comprehensive course with real life projects, assignments, demos, and cases to give hands-on experience to the participants.

Mastering Python and using its packages:

This course leads one to gain in-depth knowledge of PROC SQL, SAS Macros, and various statistical procedures, like PROC UNIVARIATE, PROC MEANS, PROC FREQ, AND PROC CORP.

Mastering advanced analytics techniques:

The course elucidates advanced analytics techniques like clustering decision tree and regression.As an added advantage one would be provided with 4 real-life industry projects on customer segmentation, macrocalls, attrition analysis, and retail analysis.

What are the course objectives :
This course enables one to:
  1. edge on the concepts of data science process, data wrangling, data exploration, data visualization, hypothesis building, and testing. one will learn the basics of statistics.
  2. install python environment, auxilary tools and other libraries.
  3. gaining adequate knowledge on essential concepts of python programming like datatypes, tuples,lists, dicts,basic operators and functions
  4. executing high level maths using Numpy package and its large library of mathemantical functions.
  5. performing scientifical and technical computing using Scipy package and its sub packages such as integrate,optimise,statistics,IO and weave
  6. perform data analysis manipulation using data structures and tools provided in pandas package.
  7. to become experts in machine learning using the Scikit-learn package.
  8. acquire in-depth knowledge of supervised and unsupervised learning models like linear regression, logistic regression, clustering, dimensionality reduction, KNN, and pipeline.
  9. Implement Scikit - learning for natural language processing.
  10. usage of matplotlib library of python for data visualization.
  11. to extract fruitful data from websites by performing web scrapping using python.
Who should take this course? :

Data science has become a rage these days. as there is a booming demand for data scientists one can easily excel if has strong fundamentals. We recommend this Data Science training especially for the following professionals:

  1. Analytics professionals who want to work with Python
  2. Software professionals looking for a career switch in the field of analytics
  3. IT professionals interested in pursuing a career in analytics
  4. Graduates looking to build a career in Analytics and Data Science
  5. Experienced professionals who would like to harness data science in their fields
  6. Anyone with a genuine interest in the field of Data Science
Prerequisites :
  • There are no prerequisites for this course. The Python basics course included with this course provides an additional coding guidance.
What projects are included in this course?

The course includes four real-life, industry-based projects. Successful evaluation of one of the following projects is a part of the certification eligibility criteria:

Project-1: NYC 311 Service Request Analysis

Telecommunication: Perform a service request data analysis of New York City 311 calls. You will emphasize on the data wrangling techniques to understand the pattern in the data and also visualize the major complaint types.

Project-2: MovieLens Dataset Analysis

Engineering: The GroupLens Research Project is a research group in the Department of Computer Science and Engineering at the University of Minnesota. The researchers of this group are involved in many research projects related to the fields of information filtering, collaborative filtering, and recommender systems. Here, we ask you to perform the analysis using the Exploratory Data Analysis technique for user datasets.

Project-3: Stock Market Data Analysis

Stock Market: As a part of the project, you need to import data using Yahoo data reader of the following companies: Yahoo, Apple, Amazon, Microsoft, and Google. Perform fundamental analytics including plotting closing price, plotting stock trade by volume, performing daily return analysis, and using pair plot to show the correlation between all the stocks.

Project-4: Titanic Dataset Analysis

Hazard:On April 15, 1912, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This tragedy shocked the world and led to better safety regulations for ships. Here, we ask you to perform the analysis through the exploratory data analysis technique. In particular, we want you to apply the tools of machine learning to predict which passengers survived the tragedy.

Exam & certification
What do I need to do to unlock my Coepd certificate?

To become a Certified Data Scientist with Python, you must fulfill the following criteria:

  1. Complete any one project out of the two provided in the course.Submit the deliverables of the project in the LMS which will be evaluated by our experts.
  2. one has to attend the complete batch training, have to complete 85% of the course and secure a minimum grade of 60%in any one of the simulation tests.

Note:one will get ceritificate only when he had completed the course, An experience certificates of 3 months would be given for implementing the projects using python.

It is mandatory to pass at least one exam with a minimum grade of 60% to be a certified professional.

FAQs?

Who provides the certification?

COEPD, the pioneers of online training and certification would provide a certificate after meeting the requirements for the grade of minimum 80% after successful training and evaluation of the projects.fulfilling the criteria one is called as a Certified data scientist.

Who are our Faculties and how are they selected?

we are blessed to have dedicated, experienced and expertise subject matter experts and industry professionals who have the successful track record of more than 12 years. Each of them has gone through a rigorous selection process which includes profile screening, technical evaluation, and training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating continue to train for us

What is Global Teaching Assistance?

Our teaching assistants are here to help you get certified in your first attempt.
There is a dedicated team of subject matter experts to help you at every step and enrich your learning experience from class onboarding to project mentoring and job assistance. They engage with the students proactively to ensure the course path is followed. Teaching Assistance is available during business hours.