Course Details

BECOME AN EXPERT IN DATA SCIENCE StatiSticS | Machine learning | NLP | R | PythoN About the Course Data Science is the study of the generalizable extraction of knowledge from data. Being a data Scientist requires an integrated skill set spanning mathematics, statistics, machine learning, databases and programming languages along with a good understanding of the craft of problem formulation to engineer effective solutions. This course will introduce students to this rapidly growing field and equip them with some of its basic principles and tools as well as its general mindset. - Students will learn concepts, techniques and tools they need to deal with various facets of data science practice, including data collection and integration, exploratory data analysis, predictive modeling, descriptive modeling, data product creation, evaluation, and effective communication. - The focus in the treatment of these topics will be a balanced approach on breadth and depth, and emphasis will be placed on integration and synthesis of concepts and their application to real time problems. - To make the learning contextual, real datasets from a variety of disciplines will be used. Program Highlights  Most Comprehensive Curriculum  Trained by passionate and Industry experts  Each concept will be explained by golden rule Theory  Example  Software Implementation (R/Python) Real-Time applicability  Designed for the Industry  Live Project  Placement Assistance Audience Any degree. No programming and Statistics knowledge required. Duration & Mode of Training  3 months, Online Training Course Content INTRODUCTION Introduction to Data Science – the 3 W’s  What is Data Science?  Why now?  Where Data Science is applicable? DATA EXPLORATION USING STATISTICAL METHODS – DESCRIPTIVE AND INFERENTIAL STATISTICS Introduction to statistics Summarizing Data  Central Tendency measures – Mean, Median and Mode  Measures of Variability – Range, Interquartile Range, Standard Deviation and Variance  Measures of Shape – Skewness and Kurtosis  Covariance, Correlation Data Visualization  Histograms  Pie charts  Bar Graphs  Box Plot Probability basics Parametric and Non parametric Statistical Tests  ‘f’ Test  ‘z’ Test  ‘t’ Test  Chi-Square test Probability Distributions  Expected value and variance  Discrete and Continuous  Bernoulli Distribution  Binomial Distribution  Poisson Distribution  Normal Distribution  Exponential Distribution  Empirical Rule  Chebyshev’s Theorem Sampling methods and Central Limit Theorem  Overview  Random sampling  Stratified sampling  Cluster sampling  Central Limit Theorem Hypothesis Testing  Type I error  Type II error  Null and Alternate Hypothesis  Reject or Acceptance criterion  P-value Confidence Intervals ANOVA  Assumptions  One way  Two way MACHINE LEARNING – INTRODUCTION Introduction to Machine Learning  What is Machine Learning?  Statistics (vs) Machine Learning  Types of Machine Learning - Supervised Learning - Un-Supervised Learning - Reinforcement Learning SUPERVISED MACHINE LEARNING Classification  Nearest Neighbor Methods (knn)  Logistic Tree based Models – Decision Tree  Basics  Classification Trees  Regression Trees Probabilistic methods  Bayes Rule  Naïve Bayes Regression Analysis  Simple Linear Regression  Assumptions  Model development and interpretation  Sum of Least Squares  Model validation  Multiple Linear Regression Regression Shrinkage Methods  Lasso  Ridge Advanced Models – Black Box  Support Vector Machine  Neural Networks Ensemble Models  Bagging  Boosting  Random Forests Optimization  Gradient Descent (Batch and Stochastic) Recommendation Systems  Collaborative filtering - User based filtering - Item based filtering UNSUPERVISED MACHINE LEARNING Association Rules (Market Basket Analysis)  Apriori Cluster Analysis  Hierarchical clustering  K-Means clustering Dimensionality Reduction  Principal Component Analysis  Discriminant Analysis (LDA/GDA) MODEL VALIDATION Confusion Matrix ROC Curve (AUC) Gain and Lift Chart Kolmogorov-Smirnov Chart Root Mean Square Error (RMSE) Cross Validation  Leave one out cross validation (LOOCV)  K-fold cross validation NATURAL LANGUAGE PROCESSING Introduction to Natural Language Processing Sentiment Analysis Text Similarity R Programming Language Introduction  R Overview  Installation of R and RStudio software  Important R Packages  Datatypes in R – Vectors, Lists, Matrices, Arrays, Data Frames Decision making & Loops  If-else, while, for  Next, break. try-catch Functions  Writing functions  Nested functions Built-in functions  Vapply, Sapply, Tapply, Lapply etc. Data Preparation/Manipulation  Reading and Writing Data  Summarize and structure of data  Exploring different datasets in R  Sub Setting Data Frames  String manipulation in Data Frames  Handling Missing Values, Changing Data types, Data Binning Techniques, Dummy Variables Data Visualization using ggplot2  Basic charts – Histograms, Bar plots, Line graphs, Scatter plots etc. Python Programming Language Introduction  How is Python different from R  Installing Anaconda- Python  Setting up with spyder Datatypes in Python Importing modules Introduction to Strings String manipulation Control loops:  For  While  If else Functions  Lambda  apply Numpy Pandas  Introduction to Dataframes  Conversion of written R codes into python Scipy-Machine Learning in Python Beautiful Soup Matplotlib

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