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Course Description:

  1. Welcome! I am delighted to welcome you into the course of Data Science. In this course, you will learn both the basics of conducting data science and how to perform data analysis in python. Prerequisites This course is intended for learners who have a basic knowledge of programming in any language (Java, C, C++, Pascal, Fortran, JavaScript, PHP, python, etc.). Course Overview First, and foremost, you'll learn how to conduct data science by learning how to analyse data. That includes knowing how to import data, explore it, analyse it, learn from it, visualize it, and ultimately generate easily shareable reports. We'll also introduce you to two powerful areas of data analysis: machine learning and natural language processing To conduct data analysis, you'll learn a collection of powerful, opensource, tools including:
  2. python
  3.  jupyter notebooks 
  4. pandas
  5. numpy
  6. matplotlib
  7. scikit learn
  8. nltk
  9. And many other tools Learning Objectives
  10. Basic process of data science
  11. Python and Jupyter notebooks
  12. An applied understanding of how to manipulate and analyse uncurated datasets
  13. Basic statistical analysis and machine learning methods
  14. How to effectively visualize results By the end of the course, you should be able to find a dataset, formulate a research question, use the tools and techniques of this course to explore the answer to that question and share your findings. Course Outline The course is broken into 16 weeks. The beginning of the course is heavily focused on learning the basic tools of data science, but we firmly believe that you learn the most about data science by doing data science. So, the latter half of the course is a combination of working on large projects and introductions to advanced data analysis techniques. Module 1. Introduction ● Python - Variables and data types
  15.  Python - Data Structures in Python
  16. Python - Functions and methods
  17. Python - If statements 
  18. Python - Loops
  19. Python - Python syntax essentials
  20. Python - Writing/Reading/Appending to a file
  21. Python - Common pythonic errors
  22. Python - Getting user Input
  23. Python - Stats with python
  24. Python - Module Import
  25. Python - List and Multidimensional lists
  26. Python - Reading from CSV
  27. Python - Multi Line Print
  28. Python - Dictionaries
  29. Python - Built in functions
  30. Python - Built in Modules Module 2. Jupyter and Numpy
  31. Python Numpy - Introduction
  32. Python Numpy - Creating an Array
  33. Python Numpy - Reading Text Files
  34. Python Numpy - Array Indexing
  35. Python Numpy - N-Dimensional Arrays
  36. Python Numpy - Data Types
  37. Python Numpy - Array Math
  38. Python Numpy - Array Methods
  39. Python Numpy - Array Comparison and Filtering
  40. Python Numpy - Reshaping and Combining Arrays Module 3. Pandas and Matplotlib
  41. Python Pandas – Introduction
  42. Introduction to Data Structures
  43. Python Pandas – Series
  44. Python Pandas – DataFrame
  45. Python Pandas – Basic Functionality
  46. Python Pandas – Descriptive Statistics
  47. Python Pandas – Indexing and Selecting Data
  48. Python Pandas – Function Application
  49. Python Pandas – Reindexing
  50. Python Pandas – Iteration
  51. Python Pandas – Sorting
  52. Python Pandas – Working with Text Data
  53. Python Pandas – Options and Customization
  54. Python Pandas – Missing Data
  55. Python Pandas – GroupBy
  56. Python Pandas – Merging/Joining
  57. Python Pandas – Concatenation
  58. Python Pandas – IO Tools
  59. Python Pandas – Dates Conversion Module 4. R for Data Science
  60. Introduction to R Programming
  61. Importance of R
  62. Data Types and Variables in R
  63. Operators in R
  64. Conditional Statements in R
  65. Loops in R
  66. R script and Functions in R
  67. Building Web Application using Rshinny Module 5. SQL for Data Science
  68. Install SQL packages and Connecting to DB
  69. Basics of SQL DB, Primary key, Foreign Key
  70. SELECT SQL command, WHERE Condition
  71. Retrieving Data with SELECT SQL command and WHERE Condition to Pandas Data frame
  72. SQL JOINs
  73. Left Join, Right Joins, Multiple Joins Module 6. Machine Learning - Introduction
  74. What is Machine Learning
  75. Types of Machine Learning
  76. Applications of Machine Learning
  77. Supervised vs Unsupervised learning
  78. Classification vs Regression
  79. Training and testing Data
  80. features and labels Module 7. Linear Regression
  81. Introduction
  82. Introducing the form of simple linear regression
  83. Estimating linear model coefficients
  84. Interpreting model coefficients
  85. Using the model for prediction
  86. Plotting the "least squares" line
  87. Quantifying confidence in the model
  88. Identifying "significant" coefficients using hypothesis testing and pvalues
  89. Assessing how well the model fits the observed data
  90. Extending simple linear regression to include multiple predictors
  91. Comparing feature selection techniques: R-squared, p-values, crossvalidation
  92. Creating "dummy variables" (using pandas) to handle categorical predictors Module 8. Logistic Regression
  93. Refresh your memory on how to do linear regression in scikit-learn
  94. Attempt to use linear regression for classification
  95. Show you why logistic regression is a better alternative for classification
  96. Brief overview of probability, odds, e, log, and log-odds
  97. Explain the form of logistic regression
  98. Explain how to interpret logistic regression coefficients
  99. Demonstrate how logistic regression works with categorical features
  100. Compare logistic regression with other models Module 9. Support Vector Machine
  101. Introduction
  102. Tuning parameters
  103. Kernel
  104. Regularization
  105. Gamma
  106. Margin
  107. Classification Example Module 10. Naive Bayes
  108. Introduction
  109. Working Example Module 11. K-Means Clustering
  110. Introduction 
  111. Unsupervised Learning
  112. K-Means Algorithm
  113. Optimization Objective
  114. Random Initialization
  115. Choosing the number of clusters Module 12. KNN
  116. Introduction
  117. Working Example Module 13. Artificial Neural Network
  118. Introduction 
  119. Cost Function
  120. Backpropagation Algorithm 
  121. Working Example Module 14. Natural Language Processing
  122. Introduction to NLTK
  123. Stop words
  124. Stemming
  125. Lemmatization
  126. Named entity recognition 
  127. Text classification
  128. Sentiment analysis Module 15. Project Section
  129. Python Project -Introduction
  130. Python Project -Housing Data Set
  131. Python Project -Understand the problem 
  132. Python Project -Hypothesis Generation
  133. Python Project -Get Data
  134. Python Project -Data Exploration
  135. Python Project -Data Pre-Processing
  136. Python Project -Feature Engineering
  137. Python Project -Model Training
  138. Python Project -Model Evaluation Module 16. Google Cloud for Data Science
  139. Introduction to the Data and Machine Learning on Google Cloud
  140. Recommending Products using Cloud SQL and Spark
  141. Predict Visitor Purchases Using BigQuery ML
  142. Deriving Insights from Unstructured Data using Machine Learning
  143.  Summary

Topics covered:

Data Science

Institute Info

Faculty : MOAHMMED
Duration : 4 MONTHS Days
Course Fee : 41,300
Training Type : Online
Batch Type : Regular, Weekend, Fastrack

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