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Data Science Course In Hindi
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1. Introduction to Data Science
2: Python (Core)
- Python A to Z full Course In Hindi
- Python setup
- Python IDE
- Variables
- Escape Sequence In Python
- Row String in python
- How To Print Imoji in python
- Arithmetic Operator in python
- Python Operators
- String In Python
- Input Function in Python
- int function python
- String Formatting Python
- String indexing & String Slicing
- Len Function
- Lower & Upper Function
- Count Function
- String Indexing
- Title Function
- Find Function
- Replace Function
- Range Function & Step Argument
- Center Method
- Comparison Operator
- Operator In Python
- Or & And Operator
- Keyword in python
- If Statement
- Pass Statement
- If Else Statement
- If Elif Else Statement
- Nested If Else
- Break Statement
- Continue Statement
- For Loop
- While Loop
- Home Assignment
- String
- List
- Dictionary
- Tuple
- Set
- Functions
- Lambda Expression
- Map Function
- Zip Function
- Any & all Function
- Reduce Function
- Max & Min Function
- Filter Function
- Python Numpy
- OOPS
- Module
- Exception
- Home Assignment
- Python File Handling
- File Operation
- File Reading
- File Writing
- Appending File
- Python Delete Files
3: Scientific Used in Python for Data Science
- Understand IDE Jupyter notebook & Customize Settings
- NumPy
- Pandas
- Scipy
- scikit-learn
- Matplotlib
- scify
- statmodels
- nltk
- etc
4: Accessing / Importing and Exporting Data using Python Modules
- Importing Data from various sources – Csv File, txt, excel, access etc
- Database Input (Connecting to database)
- Viewing Data objects – sub setting, methods
- Exporting Data to various formats
- Important python modules: Pandas, beautiful soup etc.
5: Data Manipulation – Cleansing – Mugging using Python Modules
- Cleansing Data with Python
- Data Manipulation steps(Sorting, filtering, duplicates, merging, appending, sub setting, derived variables, sampling, Data type conversions, renaming, formatting etc)
- Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
- Python Built-in Functions (Text, numeric, date, utility functions)
- Python User Defined Functions
- Stripping out extraneous information
- Normalizing data
- Formatting data
- Important Python modules for data manipulation (Pandas, Numpy, re, math, string, date time etc)
6: Data Analysis – Visualization using Python
- Introduction exploratory data analysis
- Descriptive statistics, Frequency Tables and summarization
- Univariate Analysis (Distribution of data & Graphical Analysis)
- Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
- Creating Graphs- Bar/pie/line chart/histogram/ box plot/ scatter/ density etc)
- Important Packages for Exploratory Analysis -NumPy Arrays, Matplotlib, seaborn, Pandas and scipy stats etc
7: Basic Statistics & Implementation of Stats Methods in Python
- Basic Statistics – Measures of Central Tendencies and Variance
- Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
- Inferential Statistics -Sampling – Concept of Hypothesis Testing
- Statistical Methods – Z/t-tests (One sample, independent, paired), Anova, Correlation and Chi-square
- Important modules for statistical methods: Numpy , Scipy , Pandas
8: Python: Machine Learning – Predictive Modeling – Basics
- Introduction to Machine Learning & Predictive Modeling
- Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting
- Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
- Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
- Over fitting (Bias-Variance Trade off) & Performance Metrics
- Feature engineering & dimension reduction
- Concept of optimization & cost function
- Concept of gradient descent algorithm
- Concept of Cross validation(Bootstrapping, K-Fold validation etc)
- Model performance metrics (R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )
9: Machine Learning Algorithms & Applications – Implementation in Python
- Linear & Logistic Regression
- Segmentation – Cluster Analysis (K-Means)
- Decision Trees (CART/CD 5.0)
- Ensemble Learning (Random Forest, Bagging & boosting)
- Artificial Neural Networks(ANN)
- Support Vector Machines(SVM)
- Other Techniques (KNN, Naïve Bayes, PCA)
- Introduction to Text Mining using NLTK
- Introduction to Time Series Forecasting (Decomposition & ARIMA)
- Important python modules for Machine Learning
- (SciKit Learn, stats models, scipy, nltk etc)
- Fine tuning the models using Hyper parameters, grid search, piping etc.
Data Science Course In Hindi
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