The Topics You Have To Learn About Data Science in 2023

                                   DATA SCIENCE

The Topics You Have To Learn About Data Science in 2023
 

         Introduction to Data science

  • Data Science is the study of data, where we apply statistical techniques and extract
  • insights from the data which helps organizations in better informed decision making. 

Course Content 

Introduction to Data science

  • Why? What? How?. Role and Responsibilities of Data Analyst
  •  Data science vs Data Analyst vs Data engineer

                      Introduction to Excel

  • Introduction
  • Data Preparation & Data Modules Fundamentals
  • Data Preparation & Visualization               Advanced-Templates,R scripting Tooltips
  • Intermediate Data Transformation             Para meters & Functions  
  • Intermediate Inter Active Visualization     DAX - The Essentials
  • Intermediate Data Transformation             DAX - Advanced
  • Intermediate Inter Active Visualization
  • Advanced Visualization
    

  • SQL
  • Introduction & Installation
  • DDL - Create, Alter, Drop & Truncate
  • DML - Insert, DQL - Select
  • DML - Update, Delete, Where Clause, Import Data, Export Data
  • Operators - Arithmetic, Comparison, Logical - And, Or, Not
  • Operators - Between, Like, Wildcard, RegExp Is Null, Is Not Null In, Distinct, Limit
  • Aggregate Function - SUM, MIN, MAX, COUNT, AVG, ROUND, STD, SQUARE,
  • POWER, FLOOR, CEILING
  • Order By, Group By, Having, Alias, Clone Table, Views, Subquery, Handling
  • Duplicates
  • Date Function - CURDATE, ADDDATE, ADDTIME, CURTIME, DATE_FORMAT, NOW,
  • MONTH, MONTHNAME, DAY, EXTRACT, DAY, DAYOFMONTH, DAYOFWEEK,
  • DAYOFYEAR,, DATEDIFF
  • Joins - Inner Join, Left Join, Right Join Using Function
  • TCL - SavePoint, Rollback, Commit Constraints - Primary Key, Foreign Key, Null,
  • Not Null, Unique, Auto_Increment
  • DCL - Grant, Revoke Create User, Alter User, Drop User
  • Store Procedure, Index, SQL Injection, Windows Function

                        

 Power BI

                            Topic   

Understanding Power BI

Download & Install

The Three Views In Power BI

Important: Initial Settings

Query Editor - Basic data cleaning

Working with the attached project files

Edit rows & columns,Data Types,Replacing ValuesData Types, Replace & Edit rows

 Data Preparation & Data Modules Fundamentals

Extracting values,Split columns,Text operations,Numerical operations

Creating relationships (data model)

Stacked column chart & Pie chart

 Data Preparation & Visualization

Append Queries, Merge & Group, Dates & Hierarchies, Line Chart

Files from a folder, Fact-Dimension modelEdit relationships & cardinality

Activate & deactivate relationships

Manage & autodetect relationships

 Intermediate Data Transformation

Tables, Customizing tables, Merging Queries, Unpivot & Pivot&Many-to-Many Relationship,

Filter Visual

Intermediate Inter Active VisualizationFilters Pane, Top N Filter,]Sync Slicers, Treemap

Visuals, Edit interactions, Drillthroughs, Keep filters with drill through, Tooltips

Custom column, Enable & Disable Load, References vs. Duplicates. Columns from example

 Advanced Visualization

Visual Header & Sorting, Conditional Coloum, Maps, filled maps,Forecast

Drill Through with Button, Books marks, Top products,Cards, Multi Row Cards

  Power BI

    Topics            

Para meters & Functions

Get data from a web page, Use parameters with a web page   

                                          Understanding Calculated Columns, Understanding,

MeasuresAVERAGE, COUNT, DISTINCT COUNT, COUNTROWS

SUM,AVERAGEX & ROUND

RELATED & Data Model, CALCULATE,Filter problems

FILTER

    Logical operators

                                                                    DAX - Advanced

ALL

ALL on columns

  ALL EXCEPT

ALL SELECTED

     DATEADD 

Year-to-Date & Month-to-Date

       ROUNDING functions

                                                                           FORMAT

 DATA SCIENCE

 Python


1. Introduction to Python for Data Science                           24. Membership Operators

2. Install and Write Your First Python Code                         25. If Statement  

3. Introduction to Jupyter Notebook And Jupyter Lab         26. If...Else Statement

4. Keywords And Identifiers                                                27. ELif Statement

5. Python Comments                                                            28. For loop  

6. Python Variables                                                               29. While loop 

7. Rules and Naming Conventions for Python                     30. Break and Continue Statement 

   Variables                                                                            31. User Define Functions    

8. Integer & Floating Point Numbers                           32. Arbitrary Arguments 

9. Complex Numbers                                                            33. Function With Loops  

10. Strings                                                                             34. Lambda Function 

11. LIST                                                                                35. Built-In Function 

12. Tuple                                                                               36. Global Variable

13. Set                                                                                   37. Local Variable

14. Dictionary                                                                       38. File Handling in Python  

15. Range In Python                                                             39. The Close Method  

16. List Comprehension                                                       40. The With Statement 

17. Input() Function In Python                                             41. Writing To A File In Python 

18. Arithmetic Operators                                                      42. Python Modules  

19. Comparison Operators                                                   43. Renaming Modules 

20. Logical Operators                                                           44. The from...import Statement  

21. Bitwise Operators                                                           45. Python Packages and Libraries 

22. Assignment Operators                                                    46. PIP Install Python Libraries

23. Special Operators                                 


PYTHON NUMPY                                                PYTHON PANDAS

 

1. Introduction To Numpy                              1. Pandas- Series   

2. Creating Multi-Dimensional Numpy         2. Loc & iLoc

Arrays                                                             3. Operations On Pandas DataFrame   

3. Arange Function                                         4. Selection And Indexing On Pandas DataFrame

4. Zeros, Ones and Eye functions                  5. Reading A Dataset Into Pandas DataFrame

5. Reshape Function                                      6. Adding A Column To Pandas DataFrame

6. Linspace                                                    7. How To Drop Columns And Rows In Pandas DataFrame

7. Resize Function                                        8. How To Reset Index In Pandas Dataframe  

8. Indexing & Slicing                           9. How To Rename A Column In Pandas Dataframe

9. Broadcasting                                            10. Tail(), Column and Index  

10. How To Create A Copy Dataset             11. How To Check For Missing Values or Null Values(isnull() Vs Isna())                               

11. Introduction Creating Matrix                 12. Pandas Describe Function

                                                                     13. Conditional Selection With Pandas

                                                                     14. How To Deal With Null Values

                                                                     15. How To Sort Values In Pandas

                                                                     16. Pandas Groupby

                                                                     17. Count() & Value_Count()

                                                                     18. Concatenate Function

                                                                     19. Join & Merge(Creating Dataset)

                                                                     20. Pandas-Join

                                                                     21. Pandas- Merge      

                           DATA SCIENCE   
  •  DATA VISUALISATION: MATPLOTLIB AND SEABORN

  1. Matplotlib Subplots
  2. Seborn 
  3. Scatterplot
  4. Correlation 
  5. Boxplot 
  6. Pie Chart 
  7. Heatmap 
  8. Univariate Plots 
  9. Bivariate Plots 
  10. Multivariate Data Visualisation
  • MACHINE LEARNING (ML)
  1. Introduction To Machine Learning 
  2. Practical Understanding Of Machine Learning 
  3. Applications of Machine Learning 
  4. Machine Learning Life Cycle 
  5. Setting Up Your Environment for Machine Learning 
  6. Machine Learning Algorithms 
  7. How Machine Learning Algorithms 
  8. Learn Difference Between Algorithm and Model 
  9. Supervised vs Unsupervised ML 
  10. Dependent vs Independent Variables 



1. Simple LINEAR REGRESSION, Multiple Linear Regression, Polynomial Regression                                               What is Regression? 
    
2. LOGISTIC REGRESSION ALGORITHM
       Regressor Algorithm Vs Classifier Algorithm Limitations                                                                                                      of Linear Regressio
    
3. NAIVE BAYES ALGORITHM (NB


1.LINEAR REGRESSION, Multiple Linear Regression, Polynomial RegressionSimple What is Regression? Introduction to Linear Regression Conceptual Understanding of Linear Regression MSE vs RMSE 

2. LOGISTIC REGRESSION ALGORITHM Regressor Algorithm Vs Classifier Algorithm Limitations of Linear Regression 

3. NAIVE BAYES ALGORITHM (NB) 

4. K-NEAREST NEIGHBOR ALGORITHM (KNN) 

5. SUPPORT VECTOR MACHINE ALGORITHM (SVM) 

6. MACHINE LEARNING ALGORITHM PERFORMANCE METRICS 

7. OVERFITTING AND UNDERFITTING 

8. DECISION TREE ALGORITHM 

9. ENSEMBLE TECHNIQUES Understanding Ensemble Techniques Difference b/n Random Forest & Decision Tree Why Random Forest Algorithm Introduction to Bootstrap Sampling | Bagging Understanding Bootstrap Sampling Adaboost Gradient Boost Gradient Boosting: An Intuitive Understanding The Mathematics behind Gradient Boosting Algorithm XGBoost 

10. K-MEANS CLUSTERING ALGORITHM 

11. HIERARCHICAL CLUSTERING ALGORITHM 

12. FEATURE ENGINEERING : MODEL SELECTION & OPTIMISATION (Ridge and Lasso Regression, PCA) 

13. SAVING AND LOADING ML MODEL 

  • WEB SCRAPING FOR DATA SCIENCE

  1. Introduction to Web Scraping libraries Request Beautifulsoup
  2. Request
  3. Beautifulsoup


  • Deep Learning (ARTIFICIAL NEURAL NETWORK)



  1. Introduction To Deep Learning
  2. What is Artificial Neural Network?
  3. Neurons and Perceptrons
  4. Machine Learning vs Deep Learning
  5. Why Deep Learning
  6. Applications of Deep Learning
  7. Neural Network: An Overview
  8. Components of the Perceptron
  9. Fully Connected Neural Network
  10. Types of Neural Networks
  11. How Neural Networks work
  12. Propagation: Forward and Back Propagation
  13. Understanding Neural Network
  14. Hands-on Forward and Back Propagation
  15. Optimizers In NN


  • Activation Functions



  1. An Introduction
  2. Sigmoid Activation Function
  3. Vanishing Gradient
  4. TanH Activation Function
  5. ReLU Activation Function
  6. Leaky ReLU Activation Function
  7. SoftMax Activation Function
  8. Computer Vision
  9. WORKING WITH IMAGES
  10. INTRODUCTION TO CONVOLUTIONAL NEURAL Networks
  11. OBJECT DETECTION
  12. PERFORMANCE METRICS FOR OBJECT DETECTION
  13. OBJECTION DETECTION TECHNIQUES
  14. OPENCV


  • Deep Learning (ARTIFICIAL NEURAL NETWORK)


  1. Computer Vision
  2. WORKING WITH IMAGES
  3. INTRODUCTION TO CONVOLUTIONAL NEURAL Networks
  4. OBJECT DETECTION
  5. PERFORMANCE METRICS FOR OBJECT DETECTION
  6. OBJECTION DETECTION TECHNIQUES
  7. OPENCV
  8. Natural Language Processing
  9. What is NLP?
  10. Applications of NLP
  11. WORKING WITH IMAGES
  12. TEXT PRE-PROCESSING
  13. RECURRENT NEURAL NETWORK (RNN)
  14. What is a Recurrent Neural Network (RNN)?
  15. Types of RNNs
  16. Use Cases of RNNs
  17. Long-Short Term Memory (LSTM)


  • MLOps 0verview


  1. What is MLOps?
  2. MLOps Lifecycle
  3. ML Development
  4. Model Building and Training
  5. Training Operationalisation
  6. Model Versioning
  7. Model Registry
  8. Model Governance
  9. Model Deployment
  10. Prediction Serving
  11. Model Monitoring


  • CRISP -DM


  1. What is CRISP-DM
  2. Six sequential Phases of CRISP-DM
  3. Business understanding
  4. Data understanding
  5. Data preparation
  6. Modeling
  7. Evaluation
  8. Deployment
  9. What is CRISP-DM
  10. Six sequential Phases of CRISP-DM


  • DATA SCIENCE


  • STATISTICS FOR DATA SCIENCE


  1. Introduction to Statistics For Data Science
  2. Why Statistics Is Important For Data Science?
  3. How Much Maths Do I Need To Know?
  4. Types Of Statistics
  5. Common Statistical Terms
  6. What Is Data?
  7. Data Types Data
  8. Attributes and Data Sources
  9. Structured Vs Unstructured Data
  10. Frequency Distribution
  11. Central Tendency
  12. Mean, Median, Mode
  13. Measures of Dispersion
  14. Variance and Standard Deviation
  15. Example of Variance and Standard Deviation
  16. Variance and Standard Deviation In Python
  17. Coefficient of Variations
  18. The Five Number Summary
  19. The Quartiles: Q1 | Q2 | Q3 | IQR
  20. Introduction To Normal Distribution
  21. Skewed Distributions
  22. Central Limit Theorem
  23. Introduction to Correlation
  24. Scatterplot For Correlation
  25. Correlation is NOT Causation
  26. Why Probability In Data Science?
  27. Probability Key Concepts
  28. Mutually Exclusive Events
  29. Independent Events
  30. Rules For Computing Probability
  31. Baye's Theorem
  32. Introduction To Hypothesis
  33. Null Vs Alternative Hypothesis
  34. Setting Up Null and Alternative Hypothesis
  35. One-tailed Vs Two-tailed test
  36. Key Points On Hypothesis Testing
  37. Type 1 vs Type 2 Errors
  38. Process Of Hypothesis testing
  39. P-Value
  40. Alpha-Value or Alpha Level
  41. Confidence Leve


  • DATA SCIENCE


  • Capstone Project


  1. Recommendation Engine
  2. Sentiments Analyzer
  3. Customers Churn


  • Course Detail


The Topics You Have To Learn About Data Science in 2023


  •  Stay Tuned For More Blogs And Updates.....

    
  

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