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Data Analytics- Python

 

Please Note: All below course content will be covered in practical scenarios and regular assignments will be shared. All sessions will be recorded and shared with student for future reference (free of cost). Along with below course.

 

  1. What is Data Science
  2. Introduction to Data Science Python Tool
  3. Introduction to Data Science Environment
  4. Data For the Course
  5. Some Miscellaneous IPython Usage Facts
  6. Online iPython Interpreter
  7. Different Types of Data Used in Statistical & ML Analysis
  8. Different Types of Data Used Programatically
  9. Python Data Science Packages To Be Used
  10. Create Numpy Arrays
  11. Numpy Operations
  12. Matrix Arithemetic and Linear Systems
  13. Numpy for Basic Vector Arithmetic
  14. Numpy for Basic Matrix Arithmetic
  15. Broadcasting with Numpy
  16. Solve Equations with Numpy
  17. Numpy for Statistical Operation
  18. Assignment
  19. Data Structures in Python
  20. Read in CSV Data Using Pandas
  21. Read in Excel Data Using Pandas
  22. Reading in JSON Data
  23. Read in HTML Data
  24. Removing NAs/No Values From Our Data
  25. Basic Data Handling: Starting with Conditional Data Selection
  26. Drop Column/Row
  27. Subset and Index Data
  28. Basic Data Grouping Based on Qualitative Attributes
  29. Crosstabulation
  30. Reshaping
  31. Pivoting
  32. Rank and Sort Data
  33. Concatenate
  34. Merging and Joining Data Frames
  35. Some Theoretical Principles Behind Data Visualization
  36. Histograms-Visualize the Distribution of Continuous Numerical Variables
  37. Boxplots-Visualize the Distribution of Continuous Numerical Variables
  38. Scatter Plot-Visualize the Relationship Between 2 Continuous Variables
  39. Barplot
  40. Pie Chart
  41. Line Chart
  42. Some Pointers on Exploring Quantitative Data
  43. Explore the Quantitative Data: Descriptive Statistics
  44. Grouping & Summarizing Data by Categories
  45. Visualize Descriptive Statistics-Boxplots
  46. Common Terms Relating to Descriptive Statistics
  47. Data Distribution- Normal Distribution
  48. Check for Normal Distribution
  49. Standard Normal Distribution and Z-scores
  50. Confidence Interval-Theory
  51. Confidence Interval-Calculation
  1. Test the Difference Between Two Groups
  2. Test the Difference Between More Than Two Groups
  3. Explore the Relationship Between Two Quantitative Variables
  4. Correlation Analysis
  5. Linear Regression-Theory
  6. Linear Regression-Implementation in Python
  7. Conditions of Linear Regression
  8. Conditions of Linear Regression-Check in Python
  9. Polynomial Regression
  10. GLM: Generalized Linear Model
  11. Logistic Regression
  12. Assignment
  13. What is Machine Learning (ML) About? Some Theoretical Pointers
  14. KMeans-theory
  15. KMeans-implementation on the iris data
  16. Quantifying KMeans Clustering Performance
  17. KMeans Clustering with Real Data
  18. How Do We Select the Number of Clusters?
  19. Hierarchical Clustering-theory
  20. Hierarchical Clustering-practical
  21. Principal Component Analysis (PCA)-Theory
  22. Principal Component Analysis (PCA)-Practical Implementation
  23. Data Preparation for Supervised Learning
  24. Pointers on Evaluating the Accuracy of Classification and Regression Modelling
  25. Using Logistic Regression as a Classification Model
  26. RF-Classification
  27. RF-Regression
  28. SVM- Linear Classification
  29. SVM- Non Linear Classification
  30. Support Vector Regression
  31. knn-Classification
  32. knn-Regression
  33. Gradient Boosting-classification
  34. Gradient Boosting-regression
  35. Voting Classifier
  36. Assignment
  37. Perceptrons for Binary Classification
  38. Getting Started with ANN-binary classification
  39. Multi-label classification with MLP
  40. Regression with MLP
  41. MLP with PCA on a Large Dataset
  42. Start With Deep Neural Network (DNN)
  43. Start with H20
  44. Default H2O Deep Learning Algorithm
  45. Specify the Activation Function
  46. H2O Deep Learning For Predictions
  47. Assignment
  48. Read in Data from Online CSV
  49. Read Data from a Database
  50. Data Imputation
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