MathClubforKids

  • 4500 Stonecrest Dr, Ellicott City, MD 21043, USA

Data Science and Machine Learning with Python

General Objectives :

Upon completion of the course, the students shall be able to:

  • Proficiency in Python programming, encompassing advanced concepts.
  • Mastery in Data Structures and Algorithms (DSA) for efficient problem-solving.
  • Expertise in numerical computing and data manipulation using libraries like NumPy and Pandas.
  • Graphical presentation of statistical diagram using Matplotlib.
  • Mastery of Machine Learning (ML), and Deep Learning (DL) principles and applications using Scikit-learn, SciPy, etc.
  • Ability to implement various algorithms and models for classification, regression, clustering, and natural language processing tasks.
  • Proficiency in Full Stack Data Science, covering data collection, cleaning, analysis, visualization, and interpretation.
  • Model Evaluation, error calculation, and finally create a proper story as per project.

Platform used :

  1. Google Colab.
  2. Trinket.
  3. Online Python Compiler

Week 1: Introduction to Python

  • What is Python, what features of Python, and why to learn Python?
  • Real-life application uses in the IT Industry and implementation.
  • Hands-on experience with Google Colab.
  • Python Syntax
  • Python Comments
  • Python Variables, Identifiers
  • Python Data Types
  • Python Keywords
  • Python Operators
  • Python If-Else 
  • Programming: Calculate sum, difference, product, division, modulo of two numbers, conversion from Fahrenheit to Celsius and vice versa, Area and parameters of triangles, squares, cones, cylinders and spheres, leap year problems, etc.

Week 2: Understanding data

  • What is Data?
  • Qualitative vs Quantitative data.
  • Primary and Secondary data with real-life examples.
  • Data representation using statistical tools.
  • Discrete vs Continuous data.
  • Bar chart, histogram, line diagram, pie chart.
  • Concept of measure of Central tendency.
  • Difference between Arithmetic Mean, Geometric Mean, and Harmonic Mean.
  • Impact of Median in our real life.
  • Calculate Mode.
  • Programming: Calculate mean, median, mode, variance, and standard deviation, find the sum of n natural numbers, etc.

Week 3: Concepts of Loop

  • Python Loop and types of loops
  • Python For loop
  • Python While loop
  • Python Break, Continue.
  • Python Strings
  • Python Lists
  • Python Tuples
  • Python Function
  • Programming: Sum of the digit, the reverse of a number, Palindrome number, Armstrong number, Krishnamurthy Number, Prime Number, Prime Factorization.

Week 4: Arithmetic, Algebraic Problems

  • Distance, time, and speed problems using Python.
  • Ratio and proportion real-life problem-solving using Python.
  • Percentage calculation
  • Simple and Compound Interest.
  • Basic Mensuration problem using Python.
  • Basic Trigonometry.
  • Application of Trigonometry using Python.
  • MCQ test
  • Coding test

Week 5: Introduction to NumPy

  • Create a Matrix.
  • Change the shape of a Matrix
  • Array Creation.
  • Array Operation.
  • Indexing and Slicing.
  • Shape Manipulation.
  • Mathematical Function.
  • Statistical Function.
  • Hands-on Problem, Real-life application.

Week 6: Basic of Probability

  • Introduction to Probability.
  • Coin Problems.
  • Dice Problems.
  • Cards Problems.
  • Concepts of Basic set theory.
  • Venn Diagram, Union, and Intersection.
  • Concepts of conditional probability
  • Bayes Theorem.
  • Find Mean, Median, Mode, Variance, and Standard Deviation using NumPy, Application of probability using NumPy.

Week 7: Introduction to Pandas

  • Introduction to Pandas Library.
  • How to create a data frame and its importance.
  • How to read data from CSV OR JSON files.
  • Data cleaning using Pandas.
  • Clean wrong format, wrong data, Duplicate values.
  • Data Manipulation.
  • Group-By function, Merging, and joining.
  • Hands-on Problem, Real-life application.

Week 8: Data Visualization

  • Introduction to Matplotlib Libraries.
  • Pyplot in Matplotlib.
  • Scatter plot.
  • Bar Chart and Histogram using Python.
  • Pie-chart using Python.
  • 3D plots.
  • Visualization tools.
  • Hands-on Problem, Real-life application.
  • MCQ Test

Week 9: Linear Regression

  • Data cleaning.
  • Supervised vs Unsupervised Learning.
  • Introduction to Scikit-Learn library.
  • Concepts of correlation.
  • Concepts of linear regression.
  • Use Linear Regression using Python.
  • Advantages and Disadvantages of linear regression.
  • Descriptive Statistics behind Correlation and Regression.
  • Mini Project.

Week 10: Application of Scikit-learn

  • Multiple Regression.
  • Classification.
  • Decision Tree.
  • Naïve Bayes.
  • Random Forest.
  • Clustering.
  • Applied Statistics behind these algorithms.
  • Project Discussion.

Week 11: AI and Deep Learning

  • Concept of AI.
  • How to use ChatGPT.
  • Introduction to OpenCV libraries.
  • Basic introduction to TensorFlow library.
  • Concepts of Deep Learning.
  • Importance of Neural Network.
  • Image recognition using deep learning.
  • Introduction to NLP.
  • Applied Statistics behind these algorithms.
  • Project Discussion.

Week 12: Final test and QNA

  • MCQ test for mathematics & Statistics.
  • Coding test for Python.
  • QNA Season.
  • Project Submission.
  • Certification.

US / Dubai / Singapore Price

$100 / month

  • Course Duration : 12 weeks/ 3 months
Your Python/Math/Olympiad/Data Course

INDIA PRICE
(For residents of India Only)

₹6000

  • Course Duration : 12 weeks/ 3 month

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Experts

Sourish Sarkar

Master of Science in QMS
Indian Statistical Institute Statistical Quality Control , Operation Research