Data Science Training In Marathahalli Bangalore

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from data in various forms, both structured and unstructured, similar to data mining.

Course Certification : Yes

Module 1

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 

Module 2

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 

Module 3

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

Module 4

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

Module 5

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, Naïve 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

Module 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, Naïve 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
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