Data Science & ML for Python-Python & Data Science Made Easy


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Course overview

Course type
Paid course
11 hours
82 lessons
Available on completion
Course author
Steven Martin
  • Python & R programming for Structured data/ tables.
  • Python in demand packages used by Data Scientist and Machine Learning professionals.
  • Basic, Inferential and Advanced Statistics
  • Concept of Linear and Logistic Regression implementing with Python code
  • Machine Learning (ML) Algorithms concepts with Python code
  • ML Algorithms - Support Vector Machine
  • Machine Learning Algorithms. - K nearest neighbors
  • Practical Application of Data Science and Machine Learning in Healthcare and Real estate Industry
  • An approach and outlook a Data Scientist and ML professional should adopt while solving business problems in real life
  • Engaging Course with Multiple choice questions for Students towards end of each section for Knowledge tests
  • Practical & Comprehensive Assignment with Guidelines explaining challenges faced by DS/ML professional and how to deal with such roadblocks.


Beginners in Python & R for Data Science: Introduction to Data science and Practical applications of Data Science and ML

This course is for Aspirant Data Scientists, Business/Data Analyst, Machine Learning & AI professionals planning to ignite their career/ enhance Knowledge in niche technologies like Python and R. You will learn with this program:

✓ Basics of Python, marketability and importance

✓ Understanding most of python programming from scratch to handle structured data inclusive of concepts like OOP,  Creating python objects like list, tuple, set, dictionary etc; Creating numpy arrays, ,Creating tables/ data frames, wrangling data, creating new columns etc.

✓ Various In demand Python packages are covered like sklearn, sklearn.linear_model etc.; NumPy, pandas, scipy  etc.

✓ R packages are discussed to name few of them are dplyr, MASS etc.

✓ Basics of Statistics - Understanding of Measures of Central Tendency, Quartiles, standard deviation, variance etc.

✓ Types of variables

✓ Advanced/ Inferential Statistics - Concept of probability with frequency distribution from scratch, concepts like Normal distribution, Population and sample

✓ Statistical Algorithms to predict price of houses with Linear Regression

✓ Statistical Algorithms to predict patient suffering from Malignant or Benign Cancer with Logistic Regression

✓ Machine learning algorithms like SVM, KNN

✓ Implementation of Machine learning (SVM, KNN) and Statistical Algorithms (Linear/ Logistic Regression) with Python programming code

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Data Science & ML for Python-Python & Data Science Made Easy
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