![Normalizing and
Scaling Data
SCALING IS IMPORTANT FOR
MODELS THAT ARE SENSITIVE
TO FEATURE MAGNITUDE.
TYPES OF SCALING:
MINMAXSCALER: TRANSFORMS
VALUES TO RANGE [0, 1]
STANDARDSCALER: CENTERS
DATA WITH MEAN 0 AND STD 1](https://image.slidesharecdn.com/learnpython-250523055219-9a74affe/85/Learn-Python-teaching-deck-learn-how-to-code-7-320.jpg)
![Data Manipulation
with Pandas
USEFUL FUNCTIONS:
.LOC[], .ILOC[], .GROUPBY(),
.AGG()
COMBINE FILTERS FOR COMPLEX
QUERIES](https://image.slidesharecdn.com/learnpython-250523055219-9a74affe/85/Learn-Python-teaching-deck-learn-how-to-code-11-320.jpg)
Learn Python teaching deck, learn how to code
- 2. Introduction to Python for Data Science WHY LEARN PYTHON FOR DATA SCIENCE? PYTHON IS BEGINNER-FRIENDLY WITH EASY-TO-READ SYNTAX. IT HAS VAST LIBRARIES TAILORED FOR DATA MANIPULATION, ANALYSIS, AND MACHINE LEARNING. IT IS WIDELY USED IN INDUSTRY AND ACADEMIA. WHAT YOU'LL LEARN IN THIS COURSE: DATA CLEANING AND PREPROCESSING EXPLORATORY DATA ANALYSIS (EDA) DATA MANIPULATION AND TRANSFORMATION BUILDING AND EVALUATING REGRESSION MODELS MAKING PREDICTIONS USING MODELS
- 3. Essential Python Libraries for Data Science PANDAS: FOR DATA MANIPULATION AND ANALYSIS USING DATAFRAMES. NUMPY: FOR NUMERICAL OPERATIONS AND ARRAY MANIPULATION. SCIPY: FOR SCIENTIFIC AND STATISTICAL COMPUTATIONS. SCIKIT-LEARN: FOR BUILDING MACHINE LEARNING MODELS. MATPLOTLIB/SEABORN (OPTIONAL): FOR DATA VISUALIZATION. THESE LIBRARIES WORK TOGETHER TO PROVIDE A COMPLETE DATA SCIENCE WORKFLOW IN PYTHON.
- 4. Loading Data into Python FUNCTIONS TO KNOW: .HEAD(): VIEW TOP ROWS .INFO(): SUMMARY OF DATA TYPES AND NULLS .DESCRIBE(): STATISTICAL SUMMARY OF NUMERICAL COLUMNS UNDERSTANDING THE STRUCTURE OF THE DATA IS THE FIRST STEP IN ANALYSIS.
- 5. Handling Missing Values MISSING DATA IS COMMON AND MUST BE HANDLED BEFORE ANALYSIS. TECHNIQUES: MEAN/MEDIAN/MODE IMPUTATION FORWARD FILL / BACKWARD FILL DROPPING MISSING ENTRIES (IF FEW)
- 6. Formatting and Standardizing Data PROPER FORMATTING ENSURES CONSISTENCY AND ACCURACY. UNIFORM FORMATS HELP PREVENT ERRORS DURING ANALYSIS.
- 7. Normalizing and Scaling Data SCALING IS IMPORTANT FOR MODELS THAT ARE SENSITIVE TO FEATURE MAGNITUDE. TYPES OF SCALING: MINMAXSCALER: TRANSFORMS VALUES TO RANGE [0, 1] STANDARDSCALER: CENTERS DATA WITH MEAN 0 AND STD 1
- 8. Binning and Categorizing Data BINNING CONVERTS CONTINUOUS DATA INTO CATEGORICAL DATA. USEFUL IN SEGMENTATION AND SIMPLIFYING ANALYSIS.
- 9. Exploratory Data Analysis (EDA) GOAL: UNDERSTAND THE DATA DISTRIBUTION AND DETECT PATTERNS. SUMMARY STATISTICS AND VISUALIZATIONS HELP IN HYPOTHESIS GENERATION.
- 10. Understanding Correlation CORRELATION IDENTIFIES LINEAR RELATIONSHIPS BETWEEN NUMERICAL VARIABLES. HELPS AVOID MULTICOLLINEARITY IN MODELING.
- 11. Data Manipulation with Pandas USEFUL FUNCTIONS: .LOC[], .ILOC[], .GROUPBY(), .AGG() COMBINE FILTERS FOR COMPLEX QUERIES
- 12. Creating Data Pipelines PIPELINES STREAMLINE PREPROCESSING AND MODELING. ENSURES CLEAN, REPEATABLE WORKFLOWS.
- 13. Introduction to Regression Modeling REGRESSION PREDICTS A CONTINUOUS OUTCOME (E.G., PRICE, INCOME). TYPES: LINEAR REGRESSION MULTIPLE LINEAR REGRESSION POLYNOMIAL REGRESSION USE CASES: PREDICT HOUSING PRICES ESTIMATE CUSTOMER SPENDING
- 14. Building a Linear Regression Model SPLITTING DATA ENSURES UNBIASED EVALUATION. FIT THE MODEL TO TRAINING DATA.
- 15. Evaluating the Regression Model R2 SCORE: PROPORTION OF VARIANCE EXPLAINED MSE: AVERAGE SQUARED ERROR BETWEEN ACTUAL AND PREDICTED
- 16. Making Predictions APPLY TRAINED MODEL TO NEW INPUTS USEFUL FOR BUSINESS DECISION MAKING
- 17. From Data to Decisions USE INSIGHTS TO: FORECAST TRENDS OPTIMIZE OPERATIONS PERSONALIZE CUSTOMER EXPERIENCES MACHINE LEARNING SUPPORTS DATA-DRIVEN STRATEGY.
- 18. Practice on open datasets (Kaggle, UCI) Learn classification and clustering techniques Next Steps: Data loading and cleaning Exploratory data analysis Data manipulation Regression modeling and evaluation What We Covered: Summary & What's Next?