Data Science In Hindi

Data Science In Hindi – Data Science Course In Hindi

Data Science

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Data Science Course In Hindi


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Data Science In Hindi

1. Introduction to Data Science

2: Python (Core)

Python Advanced

3: Scientific Used in Python for Data Science

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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