DATA SCIENCE BY 5 Yrs Exp AT BOYINA SOFTCON IN MARATHAHALLI

Contents Introduction To Data Science ...................................... 4
Jargon Busting ............................................................
............... 4
Analytics Problem Solving Framework...................................................
............................................... 4
Language of Data Analysts ............................................................
..... 4
Business and Data Understanding ..................................................... 4
Overview of analytics tools & their popularity ............................................. 4
Data Dictionary & Data Granularity ........................................................... 4
Data Quality & Cleaning ............................................................
...... 4
Data Preparation ............................................................
.................. 4
Data Visualization...............................................
...................................... 4
Case Study ............................................................
.......................... 4
Python For Data Science ............................................................
........ 4
Overview of Python......................................................
............................... 4
Need of Python for data science .................................................. 4
Introduction to installation of Python ............................................... 4
Introduction to Python Editors & IDE s (Canopy, pycharm, Jupyter, Rodeo, Ipython etc ) ........................................ 4
Understand Jupyter notebook & Customize Settings ........................... 4
Concept of Packages/Libraries - Important packages (NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc) .............. 4
Installing & loading Packages & Name Spaces ................................. 4
Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries etc.) .......................................................... 4
Variable & Value Labels Date & Time Values ................................ 4
Basic Operations - Mathematical - string date ................................... 4
Importing Data from various sources (csv, txt, excel, xml etc.) .......... 4
Database Input (Connecting to database) ........................................ 4
Viewing Data objects - sub setting methods .......................................... 4
Exporting Data to various formats (Different File systems)......................... 4
Important python modules (NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc) ....................................................... 4
Puzzles/Exercise ............................................................
......... 4
Data Wrangling In Python ............................................................
.. 4
Cleansing Data with Python ............................................................
. 4
Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc.) ............................................................
......................... 5
Data manipulation helpers (Operators, Functions, Packages, control structures, Loops, arrays etc.) ........................ 5
Python Built-in Functions (Text, numeric, date, utility functions) ............... 5
Python User Defined Functions (UDFs) .......................................... 5



Formatting data ............................................................
................. 5
Puzzles/Exercise ............................................................
...... 5
Introduction to Statistics ............................................................
........ 5
Basic Statistics - Measures of Central Tendencies and Variance ..........5
Building blocks - Probability Distributions - Normal distribution - Central Limit Theorem .......................................... 5
Descriptive statistics, Frequency Tables and summarization ................... 5
Univariate Analysis and Bivariate Analysis...................................... 5
Inferential Statistics -Sampling - Concept of Hypothesis Testing ............... 5
Statistical Methods - Z/t-tests (One sample, independent, paired), Anova, Correlations and Chi-square .................. 5
Data Visualization And Statistics Using Python.......................................... 5
Introduction exploratory data analysis ..................................................... 5
Descriptive statistics, Frequency Tables and summarization ............. 5
Univariate Analysis (Distribution of data & Graphical Analysis) ....................... 5
Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis) ...................................................... 5
Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density, ............................................................
.... 5
Important Packages for Exploratory Analysis and for statistical methods (NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc.) ............ 5
Case Study ............................................................
......................... 5
Introduction to Machine Learning & Predictive Modelling .................. 6
Types of Business problems - Mapping of Techniques - Regression vs. classification vs. segmentation vs. Forecasting ......................................... 6
Machine Learning Framework ....................................................... 6
Major Classes of Learning Algorithms -Supervised vs. Unsupervised Learning ............................................................ 6
Different Phases of Predictive Modelling (Data Pre-processing, Sampling, Model Building, Validation) .................... 6
Over fitting (Bias-Variance Trade off) & Performance Metrics .................... 6
Feature engineering & dimension reduction (PCA) ......................... 6
Concept of optimization & cost function ......................................... 6
Overview of gradient descent algorithm ............................................. 6
Overview of Cross validation (Bootstrapping, K-Fold validation etc.) ............................................................
.............. 6
Model performance metrics (R-square, adjusted R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, and confusion metrics) ............................................................
............................. 6
Linear Regression (SLR, MLR, Generalised Linear Regression, Regularization Regression) .......................................... 6
Supervised Classification (K-NN, Na ve Bayes, Logistic Regression, Support Vector Machines, Decision Trees, Neural Network) ..................... 6
Concept of Distance and related math background .................................. 6
Un-Supervised learning (K-Means Clustering, Hierarchical Clustering) ..... 6
Time series forecasting, Time Series Components (Trend, Seasonality, Cyclicity and Level) and Decomposition ...... 6
Basic Techniques of time series - Averages, Smoothening, etc. ................ 6
Advanced Techniques of time series - AR Models, ARIMA, etc. ............... 6



Understanding Forecasting Accuracy of time series - MAPE, MAD, MSE, etc. ............................................................
. 6
Concept of Ensembling and Methods of Ensembling ...................... 6
Association Rule Mining ............................................................
....... 6
Case Study and project for Applying different algorithms to solve the business problems and bench mark the results ...................................... 6




Introduction To Data Science Jargon Busting Analytics Problem Solving Framework Language of Data Analysts Business and Data Understanding Overview of analytics tools & their popularity Data Dictionary & Data Granularity Data Quality & Cleaning Data Preparation Data Visualization Case Study Python For Data Science Overview of Python Need of Python for data science Introduction to installation of Python Introduction to Python Editors & IDE s (Canopy, pycharm, Jupyter, Rodeo, Ipython etc ) Understand Jupyter notebook & Customize Settings Concept of Packages/Libraries - Important packages (NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc) Installing & loading Packages & Name Spaces Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries etc.) Variable & Value Labels Date & Time Values Basic Operations - Mathematical - string date Importing Data from various sources (csv, txt, excel, xml etc.) Database Input (Connecting to database) Viewing Data objects - sub setting methods Exporting Data to various formats (Different File systems) Important python modules (NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc) Puzzles/Exercise Data Wrangling In Python Cleansing Data with Python


Data Manipulation steps (Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, Data type conversions, renaming, formatting etc.) Data manipulation helpers (Operators, Functions, Packages, control structures, Loops, arrays etc.) Python Built-in Functions (Text, numeric, date, utility functions) Python User Defined Functions (UDFs) Formatting data Puzzles/Exercise Introduction to Statistics Basic Statistics - Measures of Central Tendencies and Variance Building blocks - Probability Distributions - Normal distribution - Central Limit Theorem Descriptive statistics, Frequency Tables and summarization Univariate Analysis and Bivariate Analysis Inferential Statistics -Sampling - Concept of Hypothesis Testing Statistical Methods - Z/t-tests (One sample, independent, paired), Anova, Correlations and Chi-square Data Visualization And Statistics 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/ boxplot/ scatter/ density, Important Packages for Exploratory Analysis and for statistical methods (NumPy Arrays, Matplotlib, seaborn, Pandas and scipy.stats etc.) Case Study


Introduction to Machine Learning & Predictive Modelling Types of Business problems - Mapping of Techniques - Regression vs. classification vs. segmentation vs. Forecasting Machine Learning Framework Major Classes of Learning Algorithms -Supervised vs. Unsupervised Learning Different Phases of Predictive Modelling (Data Pre-processing, Sampling, Model Building, Validation) Over fitting (Bias-Variance Trade off) & Performance Metrics Feature engineering & dimension reduction (PCA) Concept of optimization & cost function Overview of gradient descent algorithm Overview of Cross validation (Bootstrapping, K-Fold validation etc.) Model performance metrics (R-square, adjusted R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, and confusion metrics) Linear Regression (SLR, MLR, Generalised Linear Regression, Regularization Regression) Supervised Classification (K-NN, Na ve Bayes, Logistic Regression, Support Vector Machines, Decision Trees, Neural Network) Concept of Distance and related math background Un-Supervised learning (K-Means Clustering, Hierarchical Clustering) Time series forecasting, Time Series Components (Trend, Seasonality, Cyclicity and Level) and Decomposition Basic Techniques of time series - Averages, Smoothening, etc. Advanced Techniques of time series - AR Models, ARIMA, etc. Understanding Forecasting Accuracy of time series - MAPE, MAD, MSE, etc. Concept of Ensembling and Methods of Ensembling Association Rule Mining Case Study and project for Applying different algorithms to solve the business problems and bench mark the results