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INTRODUCTION TO DATA SCIENCE 4
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What is data Science? – Introduction Copy 30 minLecture1.1
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Importance of Data Science Copy 30 minLecture1.2
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Demand for Data Science Professional Copy 30 minLecture1.3
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Lifecycle of data science Copy 30 minLecture1.4
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Tools and Technologies used in data Science Copy 30 minLecture1.5
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Business Intelligence vs Data Science vs Data Engineer Copy 30 minLecture1.6
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Role of a data scientist Copy 30 minLecture1.7
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Fundamentals of Math and Probability 6
INTRODUCTION TO STATISTICS
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Basic understanding of linear algebra, Matrices, vectors Copy 30 minLecture2.1
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Addition and Multiplication of matrices Copy 30 minLecture2.2
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Fundamentals of Probability Copy 30 minLecture2.3
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Probability distributed function and cumulative distributed function Copy 30 minLecture2.4
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Conditional Probability Copy 30 minLecture2.5
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Class Hand-on – Problem solving using R for vector manipulation Problem solving for probability assignments Copy 30 minLecture2.6
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Descriptive Statistics 4
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Describe or summaries a set of data Measure of central tendency and measure of dispersion. Copy 30 minLecture3.1
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The mean, median, mode, Standard deviation, Variance, Range, kurtosis and skewness. Copy 30 minLecture3.2
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Histograms, Bar chart, Box plot Copy 30 minLecture3.3
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Class Hands-on- 5 Point summary Box Plot, Histogram and Bar Chart Exploratory analytics R Methods Copy 30 minLecture3.4
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Inferential Statistics 17
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What is inferential statistics Different types of Sampling techniques Central Limit Theorem Copy 30 minLecture4.1
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Univariate & Bivariate Analysis Copy 30 minLecture4.2
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Correlations Copy 30 minLecture4.3
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Least Square Regression Copy 30 minLecture4.4
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Normal Distribution Copy 30 minLecture4.5
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Binomial Distribution & Quincunx Copy 30 minLecture4.6
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Point estimate and Interval estimate Copy 30 minLecture4.7
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Creating confidence interval for population parameter Characteristics of Z- distribution and T-Distribution Basics of Hypothesis Testing Copy 30 minLecture4.8
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Bias & Variance trade-offs Copy 30 minLecture4.9
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Type of test and rejection region Copy 30 minLecture4.10
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Type of errors in Hypothesis resting, Type-l error and Type-ll errors Copy 30 minLecture4.11
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False Positive & False Negative Copy 30 minLecture4.12
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P-Value and Z-Score Method Copy 30 minLecture4.13
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T-Test, Analysis of variance(ANOVA) and Analysis of Co variance(ANCOVA) Copy 30 minLecture4.14
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Regression analysis in ANOVA Copy 30 minLecture4.15
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Problem solving for C.L.T Problem solving Hypothesis Testing Problem solving for T-test, Z-score test Copy 30 minLecture4.16
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Case study and model run for ANOVA, ANCOVA Copy 30 minLecture4.17
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Hypothesis Testing 6
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Hypothesis Testing Basics Copy 30 minLecture5.1
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Null Hypothesis Copy 30 minLecture5.2
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Alternate Hypothesis Copy 30 minLecture5.3
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p-Value Copy 30 minLecture5.4
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Type of test and Rejection Region Copy 30 minLecture5.5
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Type o errors-Type 1 Errors, Type 2 Errors P value method, Z score Method Copy 30 minLecture5.6
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Introduction to Machine Learning 6
UNDERSTANDING AND IMPLEMENTING MACHINE LEARNING
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What is Machine Learning? Copy 30 minLecture6.1
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Introduction to Supervised Learning, Unsupervised Learning & Semi-supervised Learning Copy 30 minLecture6.2
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What is Reinforcement Learning? Copy 30 minLecture6.3
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Variable Identification Copy 30 minLecture6.4
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Missing values treatment Copy 30 minLecture6.5
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Outliers treatment Copy 30 minLecture6.6
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Linear Regression 6
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Introduction to Linear Regression Linear Regression with Multiple Variables Copy 30 minLecture7.1
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Disadvantage of Linear Models Interpretation of Model Outputs Understanding Copy 30 minLecture7.2
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Multi-colinearity Copy 30 minLecture7.3
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Missing & Outlier treatment Copy 30 minLecture7.4
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Understanding Heteroscedasticity Copy 30 minLecture7.5
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Case Study – Application of Linear Regression for CTG data Copy 30 minLecture7.6
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Logistic Regression 7
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Introduction to Logistic Regression Copy 30 minLecture8.1
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Binary Logistic Regression Copy 30 minLecture8.2
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Multinomial Logistic Regression Copy 30 minLecture8.3
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Introduce the notion of classification Cost function for logistic regressio Copy 30 minLecture8.4
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Application of logistic regression to multi-class classification. Copy 30 minLecture8.5
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Confusion Matrix, Odd's Ratio and ROC Curve Advantages and Disadvantages of Logistic Regression Copy 30 minLecture8.6
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AIC & BIC Copy 30 minLecture8.7
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Decision Trees 10
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Decision Tree – C4.5, CART, CHAID Copy 30 minLecture9.1
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How to build decision tree? Understanding CART Model Classification Rules Copy 30 minLecture9.2
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Overfitting Problem Stopping Criteria And Pruning Copy 30 minLecture9.3
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Underfitting Copy 30 minLecture9.4
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Gini Index Copy 30 minLecture9.5
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Informations Gain Copy 30 minLecture9.6
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How to find final size of Trees? Model A decision Tree. Copy 30 minLecture9.7
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MDS Copy 30 minLecture9.8
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Random Forests and Support Vector Machines Interpretation of Model Outputs Copy 30 minLecture9.9
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Case Study – 1 Business Case Study for Kart Model Copy 30 minLecture9.10
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Unsupervised Learning 10
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Feature Selection & Feature Extraction Copy 30 minLecture10.1
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Feature Construction Copy 30 minLecture10.2
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Hierarchical Clustering Copy 30 minLecture10.3
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K-Means algorithm for clustering – groupings of unlabeled data points. Copy 30 minLecture10.4
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Principal Component Analysis(PCA) Copy 30 minLecture10.5
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Anomaly Detection Copy 30 minLecture10.6
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Association rules Copy 30 minLecture10.7
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Market Basket Analysis Copy 30 minLecture10.8
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Customer Segmentation Copy 30 minLecture10.9
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Dimensionality reduction on CTG Copy 30 minLecture10.10
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1. Python Introduction (PART C – PYTHON PROGRAMMING) 3
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Python background, featuresLecture11.1
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Installation and Various Python IDEsLecture11.2
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Python vs Other languagesLecture11.3
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2. Basics 6
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Operators in Python – Arithmetic, Relational, Logical and Assignment OperatorsLecture12.1
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Variables, Types Of VariablesLecture12.2
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Naming conventions• Naming conventionsLecture12.3
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String operationsLecture12.4
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Conditional statements:Lecture12.5
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Loops i) For Loops ii) While Loops iii) Break, Continue, Pass, SwitchLecture12.6
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3. Data Structures 5
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ListsLecture13.1
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TuplesLecture13.2
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SetsLecture13.3
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DictionariesLecture13.4
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ComprehensionsLecture13.5
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4. Functions and Modules 6
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Importing a module and setting pathLecture14.1
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Creating user defined modulesLecture14.2
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Overview of standard python modulesLecture14.3
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Function Overview – Defining a function and syntaxLecture14.4
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Calling a function and passing variablesLecture14.5
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Variables scope, call by value and call by referenceLecture14.6
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Functional programming 4
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MapLecture15.1
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ReduceLecture15.2
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FilterLecture15.3
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Lambda functionsLecture15.4
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6. File Handling and external integrations 5
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File operationsLecture16.1
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Exception handling using try, except, finallyLecture16.2
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Database accessLecture16.3
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Web scrappingLecture16.4
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REST API callsLecture16.5
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7. Python for Data Science 6
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• Numerical PythonLecture17.1
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a) nd array b) Subset, slicing c) Indexing d) List vs nd array e) Manipulating arrays f) Mathematical operations and apply functions g) Linear algebra operationsLecture17.2
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• PandasLecture17.3
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a) Data loading b) Series and Data frame c) Selecting rows and columns d) Position and label-based indexing e) Slicing and dicing f) Merging and concatenating g) Grouping and summarizing h) Lambda functions and pivot tables i) Data Processing, cleaning j) Missing Values k) OutliersLecture17.4
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• Data visualizationLecture17.5
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a) Introduction to Matplotlib Basic plotting Figures and sub plotting Box plot, Histograms, Scatter plots, image loading b) Introduction to Seaborn Histogram, rugged plot, hex plot and density plot Joint plot, pair plot, count plot, Heatmaps c) Plotting categorical data and aggregation of values d) Plotting Time-Series data using tsplotLecture17.6
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PART D – BIG DATA 9
1. Understanding Big Data and Hadoop
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What is Data?Lecture18.1
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Different types of DataLecture18.2
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Different types of DataLecture18.3
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What is Big Data and the purpose?Lecture18.4
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Where do we use it?Lecture18.5
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Various Big Data technologiesLecture18.6
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Why Hadoop?Lecture18.7
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Hadoop Eco systemLecture18.8
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Rack awarenessLecture18.9
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2. HDFS Architecture 4
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Hadoop 1.x vs 2.xLecture19.1
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Resource management and configuration filesLecture19.2
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HDFS Cluster architectureLecture19.3
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Data loading techniquesLecture19.4
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3. Map Reduce 6
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MapReduce ParadigmLecture20.1
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Advantages of MapReduceLecture20.2
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Architecture and various components of Map ReduceLecture20.3
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YARN and workflowLecture20.4
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Data orchestration in Map Reduce job flowLecture20.5
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Combiners, PartitionersLecture20.6
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4. Advanced Map Reduce 8
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JoinsLecture21.1
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Various Data TypesLecture21.2
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Input formatsLecture21.3
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Output formatsLecture21.4
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MR Unit testing frameworkLecture21.5
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CountersLecture21.6
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Distributed CacheLecture21.7
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Sequence FileLecture21.8
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5. Pig 8
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What is Pig and why is it required?Lecture22.1
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Pig vs MapReduceLecture22.2
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Pig components and structureLecture22.3
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Data TypesLecture22.4
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Data structuresLecture22.5
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Pig limitationsLecture22.6
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Pig LatinLecture22.7
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a)Operators b) FunctionsLecture22.8
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6. Hive 8
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What is Hive and where to useLecture23.1
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Pig Vs HiveLecture23.2
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Hive architecture and componentsLecture23.3
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Partitions, bucketsLecture23.4
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Data Types and data modelsLecture23.5
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Data loadingLecture23.6
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HiveQLLecture23.7
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a) UDF b) Index c) Views d) Joins e) PartitioningLecture23.8
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7. HBase 9
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Introduction to NoSQL DatabaseLecture24.1
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HBase storage architectureLecture24.2
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HBase componentsLecture24.3
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a) Regions b) Client c) Modes d) Ports and utilitiesLecture24.4
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AttributesLecture24.5
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Data LoadingLecture24.6
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Data ModelLecture24.7
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HBase APILecture24.8
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Zookeeper and HBaseLecture24.9
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8. Sqoop and Flume 11
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• Understand Data IngestionLecture25.1
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Introduction to Sqoop and FlumeLecture25.2
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Sqoop: Import from RDBMS to HDFSLecture25.3
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Sqoop: Import from RDBMS to HiveLecture25.4
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Sqoop JobsLecture25.5
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Flume multi agentLecture25.6
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Flume agentLecture25.7
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Flume sinksLecture25.8
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Flume channelsLecture25.9
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Executing the commandsLecture25.10
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Flume architectureLecture25.11
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9. Kafka 13
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Introduction to KafkaLecture26.1
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Kafka ProducerLecture26.2
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Kafka ConsumerLecture26.3
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InternalsLecture26.4
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a) Cluster membership and controllerLecture26.5
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b) ReplicationLecture26.6
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c) Request processingLecture26.7
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d) Data StorageLecture26.8
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e) File processingLecture26.9
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f) CompactionLecture26.10
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g) BrokerLecture26.11
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Cluster architectureLecture26.12
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MonitoringLecture26.13
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10. Oozie 7
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OverviewLecture27.1
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WorkflowLecture27.2
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Scheduling in OozieLecture27.3
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Configuration filesLecture27.4
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Monitoring and CoordinatorLecture27.5
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Time and Data triggersLecture27.6
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Oozie consoleLecture27.7
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11. Spark 15
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Spark Overview and architectureLecture28.1
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Spark ShellLecture28.2
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Spark contextLecture28.3
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RDDs (Resilient Distributed Datasets)Lecture28.4
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a) RDD OperationsLecture28.5
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b) PartitioningLecture28.6
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c) TransformationsLecture28.7
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d) ActionsLecture28.8
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e) Key-value pairLecture28.9
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f) PersistenceLecture28.10
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• Spark streamingLecture28.11
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• Spark SQLLecture28.12
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Broadcast VariablesLecture28.13
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AccumulatorsLecture28.14
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MapReduce vs SparkLecture28.15
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12. Scala 11
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Overview and backgroundLecture29.1
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Scala vs other languagesLecture29.2
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Environment setupLecture29.3
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Scala compilerLecture29.4
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ImmutabilityLecture29.5
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Variables and Various operatorsLecture29.6
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Conditional statements and LoopsLecture29.7
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Lists, Tuples, Maps and optionsLecture29.8
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ComprehensionsLecture29.9
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Functional programming in ScalaLecture29.10
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Object Oriented ProgrammingLecture29.11
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