Course description
Who Should attend & Course Pedagogy
- Person from any domain who are keen on learning Data Science for professional development.
- Software developers/ IT Professionals who are willing to move into Data Science.
- Professors from Institutes who are willing to upgrade their skills into Data Science.
- Middle and senior level executives from profit and not-for-profit organization across industries/sectors can attend the program.
Eligibility
- Any Graduate with Min 60% and Above marks
- No Experience is Mandatory
- No Programming Background Required
Selection Process
- Aptitude Test
- Personal Interview with Academic Expert
- Admission Letter & Enrollment
Assignments, Videos & Case Studies
Curated Course Syllabus
Module 1: SQL
- Introduction to Data Warehouse
- What is data warehouse
- Data warehouse architecture
- Top down approach
- Bottom up approach
- Data lake architecture
- Data lake and warehouse architecture
- Data lake vs. Data warehouse
- OLTP vs. OLAP
- Introduction to Data Warehouse
- Facts and Dimensions
- Additive Fact
- Factless Fact
- Non Additive Fact
- Semi Additive Fact
- Role playing dimension
- Junk dimension
- Degenerate Dimension
- Conformed Dimension
- Slowly Changing Dimension
- Facts and Dimensions
- Normal Forms
- Normal Forms 1NF
- Normal Forms 2NF
- Normal Forms 3NF
- Normal Forms 4NF
- Normal Forms
- Introduction to Constraints - Part 1
- Naming Rules
- Data Types
- Default Constraint
- Primary Key Constraints
- Foreign Key Constraints
- Introduction to Constraints - Part 1
- Introduction to Constraints - Part 2
- Not Null Constraint
- Unique Constraint
- Introduction to Constraints - Part 2
- Types of SQL Commands
- Data Definition Language
- Data Query Language
- Data Manipulation Language
- Data Control Language
- Transaction Control Language
- Types of SQL Commands
- Operators and Clauses
- Concatenation
- Where Clause
- Like Clause
- Between and
- Is null
- Order by
- Operators and Clauses
- Single Row Function - Part 1
- Character
- Number
- Date
- Single Row Function - Part 1
- Single Row Function - Part 2
- Conversion
- General Functions
- NVL
- NVL2
- COALESCE
- Case When
- Decode
- Single Row Function - Part 2
- Multi Row Function
- Avg
- Count
- Min
- Max
- Stddev
- Sum
- Variance
- Multi Row Function
- Joins in SQL - Part 1
- Different types of joins
- Inner
- Left Outer Join
- Right Outer Join
- Full Outer Join
- Joins in SQL - Part 1
- Joins in SQL - Part 2
- Self-Join
- Non Equi Join
- Cross Joins
- Joins in SQL - Part 2
- Joins in SQL - Part 3
- Self-Join
- Joins in SQL - Part 3
- Single Row SubQuery
- Detailed understanding of single row subquery
- Single Row SubQuery
- Multiple Rows SubQuery
- Detailed understanding of multiple row subquery
- In clause
- Any Clause
- All Clause
- Multiple Rows SubQuery
- Multiple Column Sub Queries
- Non Pairwise
- Pairwise
- Correlated Sub Queries
- Multiple Column Sub Queries
- Exists and With Clauses
- Detail understanding of Exists and With Clauses
- Exists and With Clauses
- Set Operators in SQL
- Union
- Union All
- Intersection
- Minus
- Set Operators in SQL
- Data Manipulation
- Insert Statements
- Update Statements
- Delete Statements
- Truncate
- Data Manipulation
- Views in SQL
- Simple and Complex views
- Update through views
- Views in SQL
- Sequences, Synonyms and Index
- Sequences and its uses
- Synonyms and its uses
- Index and its benefits and Drawbacks
- Sequences, Synonyms and Index
- Alter Statements in SQL
- How to add the constraints
- Alter statements
- System tables
- Alter Statements in SQL
- Multi Table Inserts
- Unconditional Inserts
- Conditional Inserts
- Condition First Insert
- Pivoting Insert
- Multi Table Inserts
- Merge in SQL
- Update using Merge
- Insert using Merge
- Merge in SQL
- Hierarchical Data Fetching
- Connect by Prior
- Hierarchical Data Fetching
- Regular Expressions
- REGEXP_LIKE
- REGEXP_INSTR
- REGEXP_SUBSTR
- REGEXP_REPLACE
- REGEXP_COUNT
- Regular Expressions
- Analytics Functions - Part 1
- Row Number
- Rank
- Dense Rank
- Analytics Functions - Part 1
- Analytics Functions - Part 2
- Row Number
- Rank
- Dense Rank
- Analytics Functions - Part 2
- Materialized Views
- Importance of Materialized Views
- Complete on Demand
- Complete on Commit
- Fast on Demand
- Explain Plan
- Materialized Views
- SQL Interview Prep - Advanced Select
- Understanding of advanced Select
- SQL Interview Prep - Advanced Select
- SQL Interview Prep U+2013 Joins
- Understanding of Joins and advanced joins
- SQL Interview Prep U+2013 Joins
Module 2: Python
- Basics of Python (Python History and Features)
- Python History and Features
- Basics of Python (Data Types Part 1)
- Data Types in Python
- Integer
- Float
- String
- Boolean
- Complex Numbers
- Basics of Python (Data Types Part 2)
- Memory Allocation in Python
- Lists
- Tuples
- Range
- Sets
- Frozen sets
- Dictionaries
- Basics of Python (Lists in Python)
- Detailed explanation of Lists and its functions
- Basics of Python (Tuples, Dictionaries and Sets)
- Detailed explanation of Tuples, Dictionaries and Sets
- Operators in Python - Part 1
- Arithmetic Operators
- Relational Operators
- Equality Operators
- Logical Operators
- Ternary Operators
- Operators in Python - Part 2
- Bitwise Operators
- Compound Assignment Operators
- Special Operators
- Identity Operators
- Membership Operators
- Precedence in Python
- Math Modules
- Input ways in Python
- Input statement
- Eval
- Command Line Arguments
- Output ways in Python
- Print and its formats
- Flow Control - Part 1
- if, elif and else
- for loops
- Flow Control - Part 2
- for loops
- while loops
- break
- continue
- pass
- String Operations in Python
- Various string related functions and its applications
- String Operations in Python - More Examples
- More examples on String operations
- Functions in Python - Part 1
- Functions and its benefits
- Types of arguments
- Positional arguments
- Keyword arguments
- Default arguments
- Variable length arguments
- Functions in Python - Part 2
- Types of variables
- Recursive functions
- Lambda functions
- filter
- Functions in Python - Part 3
- map
- reduce
- Memory allocation in functions – Function
- Aliasing
- Nested functions
- Exception Handling - Part 1
- Exception Handling and its various scenarios
- Exception Handling - Part 2
- Various syntaxes of exception handling
- Exception Handling – Example
- Custom exception handling example
- File Handling - Part 1
- Modes to open the files
- Various properties of file object
- How to write and read data from files With statement
- seek and tell commands
- File Handling - Part 2
- seek and tell commands
- os module and its functions
- Binary files
- Write data in csv files
- How to handle the files and folders in Python using os modules
- pickling and unpickling
- Regular Expressions - Part 1
- re module in Python and its functions
- re module in Python and its functions
- Object Oriented Programming - Part 1
- Introduction to OOPs
- Constructor in OOPs
- Object Oriented Programming - Part 2
- Self
- Instance variables
- Static variables
- Object Oriented Programming - Part 3
- Static variables
- Local variables
- Object Oriented Programming - Part 5
- Passing members from one class to another class
- Inner classes
- Object Oriented Programming - Part 6
- Garbage Collector
- Desctructor
- Object Oriented Programming - Part 7
- Inheritance
- Single Inheritance
- Multilevel Inheritance
- Hierarchical Inheritance
- Multiple Inheritance
- Object Oriented Programming - Part 8
- Composition and Aggregation
- Hybrid Inheritance
- Object Oriented Programming - Part 9
- super in OOPs
- Object Oriented Programming - Part 10
- Polymorphism in OOPs
- Object Oriented Programming - Part 11
- Method Overloading
- Constructor Overloading
- Method Overriding
- Numpy - Part 1
- Understanding of Numpy and its various functions
- Understanding of arrays and Matrix
- Numpy - Part 2
- Discussion on various Numpy functions
- Numpy - Part 3
- Discussion on various Numpy functions
- Pandas - Part 1
- Pandas - An introduction
- Understanding of Data Frames
- Joins between Data Frames
- Concat
- Pandas - Part 2
- Slicing and playing with the csv file and understand various functions on pandas
- Pandas - Part 3
- Understanding of various functions in Pandas
- Pandas - Part 4
- Understanding of various functions in Pandas
- Pandas - Part 5
- Understanding of various functions in Pandas
- dfply understanding
- Detailed understanding of dfply
- Data Visualization of Matplotlib
- Creating various plots using Matplotlib
- Data Visualization of Seaborn
- Creating various plots using Seaborn
- Data Preprocessing using SKLearn- Missing Value Imputation
- Traditional methods of imputation using mean, median and mode KNN Imputation
- Data Preprocessing using SKLearn - Outlier Treatment
- Outlier Treatment
- Data Preprocessing using SKLearn - Feature Scaling - Theory
- Standardization
- MinMax Scalar
- Robust Scaler
- MaxAbs Scalar
- Power Transformer
- Quantile Transformer
- Normalization
- Data Preprocessing using SKLearn - Feature Scaling – Handson
- Hands on of all the techniques using Python
- Python connection with Oracle - Part 1
- Create and Insert using Python
- Python connection with Oracle - Part 2
- Alter statement, Reading Data from csv and upload in Oracle
- Exploratory Data Analysis - Part 1
- End to end understanding of how to explore the data before applying Machine Learning
- Exploratory Data Analysis - Part 2
- End to end understanding of how to explore the data before applying Machine Learning
Module 3: Tableau
- Tableau - An introduction
- Understanding Tableau and its products
- Tableau Public
- Tableau Desktop
- Tableau Reader
- Tableau Online
- Tableau Server
- Tableau Prep
- Tableau Mobile
- Tableau - Creating first chart
- Understanding the Calculated Field concept
- If and Else statements
- Left Function
- Making data ready for visualization using Tableau Prep
- Tableau - Mode of connection
- Understanding the concept of Live and Extract connections
- Data Source filters
- Union and its usage
- Tableau - Data from Heterogeneous data sources
- Dive deep into the concept of Joins and how can we achieve in Tableau
- Applying the data joining using Tableau Prep
- Dive deep into the concept of Cross database joins
- Applying Cross database joins using Tableau Prep
- Understanding of Data Blending
- Understanding the concept of Primary and Secondary data sources
- Tableau - Data Blending in Tableau
- Dive Deep into Data Blending
- Understanding the usage of Data Joining, Cross Database Join and Data Blending
- Solving the end-to-end problem using Data Blending
- Tableau - Bins, Parameters and creating our first dashboard
- Creating the map view and understanding the idea of maps
- Creating the histogram and dynamic histogram using parameters
- Creating Donut chart
- Creating the stackbar chart
- Creating the interactive dashboard
- Tableau - Dashboard and Story
- Difference between Tiled and Floating options
- Adding the buttons to download the dashboard as ppt, image and pdf
- Creating the story
- Understanding the difference between Dashboard and Story
- Tableau - Time series analysis
- Understanding the concepts of Data dimension
- Difference between Discrete and continuous date field
- What is the relation between aggregation and Granularity?
- How to apply normal filters or Quick Filters
- Options in the Normal Filters or Quick Filters
- Understanding on Context Filters
- Tableau - Sets and Parameters for more dynamic visualization
- Create a dynamic scatter plot using the concepts of Sets and Parameters
- Understanding the concept of sets
- Understanding the concept of Static Sets
- Understanding the concept of Dynamic Sets
- How to tag parameters with sets to make it dynamic sets
- How to tag parameters with reference lines
- Tableau - Combined Sets and Formatting of the visuals
- Understanding the concept of Combined sets
- Formatting the visuals
- Adding sets as filters
- Tableau - Animation in Tableau - Data Prep
- Formatting the data as per the requirement using Tableau Prep
- Formatting the data as per the requirement using Tableau Desktop
- Advantage and Disadvantage of using Tableau Desktop as data preparation tool
- Tableau - Animation in Tableau - Visualization
- Creating the animated scatter plot using Animation concept
- How to animate the chart using page shelf
- Which data to choose as Primary data source
- Formatting the plot
- Sorting the secondary data source
- Adding show history option in the animation
- Tableau - Level of Details (Include)
- Understanding the concept of Include Function
- Creating the visual using Include function in Tableau
- Tableau - Level of Details (Include and Exclude)
- Implementing the include function on the real time use case
- Creating the visual using Include function in Tableau
- Understanding the concept of Exclude and where to use it
- Implementing the exclude function on the real time use case
- Creating the visual using exclude function in Tableau
- Tableau - Level of Details (Fixed)
- Understanding the concept of Fixed and where to use it
- Implementing the Fixed function on the real time use case
- Creating the visual using Fixed function in Tableau
- Tableau - Level of Details (Real time use cases)
- Customer Order Frequency
- Cohort Analysis
- Daily Profit KPI
- Percentage of Total
- New Customer Acquisition
- Average of top deals by city
- Actual vs. Target
- Value on the last day of the period
- Return Purchase by cohort
- Percentage difference from average across a range
- Relative Period Filtering
- User login frequency
- Proportional Brushing
- Annual Purchase frequency by customer cohort
- Comparative sales analysis
- Tableau - Table Calculation - Data Prep Part 1
- Preparing the data using Tableau Prep
- Understanding the datetime dimension and its purpose
- Tableau - Table Calculation - Data Prep Part 2
- Preparing the data using Tableau Desktop
- Pivoting using Tableau Prep
- Pivoting using Tableau Desktop
- Tableau - Table Calculation - Functions Part 1
- Running Sum
- Difference
- Percentage Difference
- Understanding Table Across, Table Down
- Fixing the calculation by dimension
- Understanding the difference between Table calculation and Calculated Field
- Dive deep on the functions Lookup, ZN
- Tableau - Table Calculation - Functions Part 2
- Moving Average
- Creating the logic for Quality assurance in the output
Module 4: Statistics
- Statistics - An Introduction
- Types of Data
- Analyzing Categorical Data
- Reading Pictographs
- Reading Bar Graphs
- Reading Pie Chart
- Two way Frequency Table and Venn Diagram
- Marginal and Conditional Distribution
- Displaying Quantitive data with Graphs
- Frequency Table and Dot plots
- Creating Histogram
- Measure of Central Tendency
- Mean
- Median
- Mode
- Measure of Spread - Part 1
- Range
- Variance
- Standard Deviation
- Interquartile Range
- Measure of Spread - Part 2
- Population
- Sample
- Difference in formula for Population parameter and
- Sample statistic
- Box and Whisker Plot
- Left Skewed and Right Skewed
- Outlier detection
- Mean Absolute Deviation
- Regression - Part 1
- Exploring bivariate numerical data
- Slope of a line
- Intercept
- Regression - Part 2.1
- Covariance
- Covariance Matrix
- Regression - Part 2.2
- Karl Pearson Correlation Coefficient
- Regression - Part 2.3
- Spearman Rank Correlation Coefficient
- Various scenarios
- Regression - Part 3
- Residuals
- Regression - Part 4
- R-Squared and RMSE
- Normal Distribution - Part 1
- Understanding Normal Distribution
- Z-Score concept
- Emperical Rule
- Density Curve
- Normal Distribution - Part 2
- More Examples in Z-score and Z-table
- Normal Distribution - Part 3
- More Examples in Z-score and Z-table
- Exploring Gaussian distribution equation
- Symmetric Distribution, Skewness, Kurtosis and KDE
- Symmetric distribution
- Skewness
- Kurtosis
- KDE
- Probability - An Introduction
- Simple Probability
- Probability - With Counting Outcome
- Sample space and its subset
- Intersection and Union of sets
- Relative complement or Difference between sets
- Universal Set and Absolute complement
- Subset, Strict, Subset and Superset
- Set operations together
- Difference between theoretical and experimental probability
- Probability - Part 2
- Statistical Significance of experiment
- Probability with venn diagram
- Addition rule of probability
- Sample space for compound space
- Compound probability of independent events
- Independent events
- Probability - Part 3.1
- Dependent Probability
- Conditional Probability
- Probability - Part 3.2
- Conditional Probability - Tree Diagram
- Conditional Probability and Independence
- Permutations and Combinations
- Permutation Formula
- Understanding of Zero factorial
- Factorial and Counting
- Combinations
- Combinatorics and Probability
- Probability using combinations
- General form
- Random Variables
- Continuous and Discrete and its distribution
- Mean (Expected value) of a discrete random variable
- Variance and Standard deviation
- Random Variables Variance
- Intuition for why independence matters for variance of sum
- Analyzing distribution of sum of two normally distributed random variables
- Analyzing distribution of sum of two normally distributed random variables
- Binomial Distribution - Part 1
- Binomial Variables
- Recognizing Binomial variables
- 10% Rule of assuming independence
- Binomial Distribution
- Binomial Distribution - Part 2
- Binomial Probability
- Binompdf and Binomcdf functions
- Mean and variance of Bernoulli distribution
- Expected value and variance of Binomial distribution
- Geometric Random Variables - Part 1
- Geometric random variables – Introduction
- Probability of a geometric random variable
- Cumulative geometric probability
- Geometric Random Variables - Part 2
- Cumulative geometric probability
- Proof of expected value of geometric random variables
- Poisson Distribution
- Detailed understanding of Poisson Distribution
- Sampling Distribution
- Sampling Distributions
- Sampling distribution of sample proportion
- Normal conditions for sampling distributions of sample proportions
- Probability of sample proportions
- Central Limit Theorem
- Inferring population mean from sample mean
- Central Limit Theorem
- Standard Error of the mean
- Confidence Interval
- Confidence intervals and margin of error
- Interpretation of Confidence interval
- Margin of error
- Margin of Error
- Condition for valid confidence interval for a proportion
- T-statistic
- Introduction to T-Statistics
- Conditions for valid t intervals
- Find critical t value
- Confidence interval for a mean with paired data
- Sample size for a given margin of error for a mean
- T-statistic confidence interval
- Small sample size confidence intervals
- Significance Tests - An Introduction
- Simple Hypothesis Testing
- Idea Behind Hypothesis Testing
- null and alternative hypothesis
- P-values and significance tests
- Type 1 and Type 2 Erros
- Comparing P-values to different significance levels
- Estimating a P-value from a simulation
- Type 1 and Type 2 Errors
- Introduction to Power in significance tests
- Power in significance test
- Constructing Hypothesis for a significance test about a proportion
- Constructing hypothesis for a significance test about a proportion
- Conditions for a z test about a proportion
- Calculating a z statistic in a test about a proportion
- Calculating a P-value given a z statistic
- Making conclusions in a test about a proportion
- Constructing Hypothesis for a signficance test about a mean
- Writing Hypothesis for a significance test about a mean
- Condition for a t test about a mean
- When to use z or t statistics in significance tests
- Calculating t statistic for a test about a mean
- Calculate p-value from t statistic
- Comparing P-value from t statistic to significance level
- Significance Test for Mean
- More on Significance Testing
- Hypothesis testing and p-values
- Small sample hypothesis test
- Large Sample proportion hypothesis testing
- Comparing two proportions
- Detailed understanding of Comparing Two Proportions
- Comparing two means
- Statistical Significance of experiment
- Difference of Sample mean distribution
- Confidence interval of difference of means
- Introduction to Chi Squared Distribution
- Chi-Square distribution introduction
- Pearson's Chi-Square test (Goodness of fit)
- Chi-Square statistic for Hypothesis testing
- Chi-square goodness of fit example
- Filling out frequency table for independent events
- Chi Square Test for Homogeneity and Association
- Contingency table chi-square test
- Chi-Squared test for Homogeneity
- Chi-Squared test for association (Independence)
- Advanced Regression
- Introduction to inference about slope in linear regression
- Conditions for Inference on slope
- Confidence interval for the slope of a regression line
- Calculating t statistic for slope of regression line
- Using a P-value to make conclusions in a test about slope
- Using a confidence interval to test slope
- Anova and Fstatistic
- Anova – Calculating SST, SSW and SSB
- Hypothesis testing with F-statistic
Module 5: Machine Learning
- Supervised Machine Learning - Classification and Regression - KNN
- How “ Classification” and “Regression”works?
- Data matrixnotation.
- Classification vs Regression(examples)
- K-Nearest Neighbors Geometric intuition with a toy example.
- Failure cases of K-NN
- Distance measures: Euclidean(L2),Manhattan(L1),Minkowski, Hamming
- Cosine Distance & Cosine Similarity
- How to measure the effectiveness of K-NN?
- Test/Evaluation time and space complexity.
- k-NN Limitations.
- Decision surface for K-NN as K changes.
- Overfitting and Underfitting.
- Need for Cross validation.
- K-fold cross validation.
- Visualizing train, validation and test data sets
- How to determine overfitting and underfitting?
- Time based splitting
- k-NN for regression.
- Weighted k-NN
- Voronoi diagram.
- Binary search tree
- How to build akd-tree.
- Find nearest neighbors using kd-tree
- Limitations of kd-tree
- Extensions.
- Hashing vs LSH.
- LSH for cosine similarity
- LSH for euclidean distance.
- Probabilistic class label
- Code Sample: Decision boundary.
- Code Samples: Cross-Validation
- Classification algorithms in various situations
- Introduction
- Imbalanced vs balanced data set.
- Multi-class classification.
- k-NN, given a distance or similarity matrix
- Train and test set differences.
- Impact of Outliers
- Local Outlier Factor(Simple solution: mean distance to k-NN).
- k-distance (A),N(A)
- reachability-distance(A,B)
- Local-reachability-density(A)
- Local Outlier Factor(A)
- Impact of Scale & Column standardization.
- Interpretability
- Feature importance & Forward Feature Selection
- Handling categorical and numerical features.
- Handling missing values by imputation.
- Curse of dimensionality.
- Bias-Variance tradeoff.
- Intuitive understanding of bias-variance.
- Revision Questions.
- Best and worst case of an algorithm
- Performance measurement of models
- Accuracy
- Confusion matrix, TPR, FPR, FNR,TNR
- Precision & recall,F1-score.
- Receiver Operating Characteristic Curve (ROC) curve and AUC.
- Log-loss.
- R-Squared/ Coefficient of determination.
- Median absolute deviation(MAD)
- Distribution of errors.
- Supervised Machine Learning - Classification - Naive Bayes
- Conditional probability.
- Independent vs Mutually exclusive events.
- Bayes Theorem with examples.
- Exercise problems on Bayes Theorem
- Naive Bayes algorithm.
- Toy example: Train and test stages.
- Naive Bayes on Text data.
- Laplace/Additive Smoothing.
- Log-probabilities for numerical stability.
- Bias and Variance tradeoff.
- Feature importance and interpretability.
- Imbalanced data
- Outliers.
- Missing values.
- Handling Numerical features (Gaussian NB)
- Multiclass classification.
- Similarity or Distance matrix.
- Large dimensionality.
- Best and worst cases.
- Supervised Machine Learning - Classification - Logistic Regression
- Geometric intuition of logistic regression
- Sigmoid function: Squashing
- Mathematical formulation of objective function.
- Weight Vector.
- L2 Regularization: Overfitting and Underfitting.
- L1 regularization and sparsity.
- Probabilistic Interpretation: Gaussian Naive Bayes
- Loss minimization interpretation
- Hyperparameter search: Grid Search and Random Search
- Column Standardization.
- Feature importance and model interpretability.
- Collinearity of features.
- Train & Run time space and time complexity.
- Real world cases.
- Non-linearly separable data & feature engineering.
- Code sample: Logistic regression, Grid Search CV, Random Search CV
- Extensions to Logistic Regression: Generalized linear models(GLM)
- Supervised Machine Learning - Regression - Linear Regression
- Geometric intuition of Linear Regression.
- Mathematical formulation.
- Real world Cases.
- Code sample for Linear Regression
- Solving Optimization Problems
- Differentiation.
- Online differentiation tools
- Maxima and Minima
- Vector calculus: Grad
- Gradient descent: geometric intuition.
- Learning rate.
- Gradient descent for linear regression.
- SGD algorithm
- Constrained optimization &PCA
- Logistic regression formulation revisited.
- Why L1 regularization creates sparsity?
- Supervised Machine Learning - Classification and Regression - Support Vector Machines (SVM)
- Geometric intuition.
- Mathematical derivation.
- why we take values +1 and -1 for support vector planes
- Loss function (Hinge Loss) based interpretation.
- Dual form of SVM formulation.
- Kernel trick.
- Polynomial kernel.
- RBF-Kernel.
- Domain specific Kernels.
- Train and run time complexities.
- nu-SVM: control errors and support vectors.
- SVM Regression.
- Cases.
- Supervised Machine Learning - Classification and Regression - Decision Trees
- Geometric Intuition of decision tree: Axis parallel hyperplanes.
- Sample Decision tree.
- Building a decision Tree: Entropy(Intuition behind entropy)
- Building a decision Tree: Information Gain
- Building a decision Tree: Gini Impurity.
- Building a decision Tree: Constructing a DT.
- Building a decision Tree: Splitting numerical features.
- Feature standardization.
- Categorical features with many possible values.
- Overfitting and Underfitting.
- Train and Run time complexity.
- Regression using Decision Trees.
- Cases
- Supervised Machine Learning - Classification and Regression - Ensemble Models
- What are ensembles?
- Bootstrapped Aggregation (Bagging)Intuition.
- Random Forest and their construction.
- Bias-Variance tradeoff.
- Train and Run-time Complexity.
- Bagging: code Sample.
- Extremely randomized trees.
- Random Forest: Cases.
- Boosting Intuition
- Residuals, Loss functions, and gradients.
- Gradient Boosting
- Regularization by Shrinkage.
- Train and Run time complexity.
- XG Boost: Boosting +Randomization
- Ada Boost: geometric intuition.
- Stacking models.
- Cascading classifiers.
- Unsupervised learning - Clustering – K means
- What is Clustering?
- Unsupervised learning
- Applications.
- Metrics for Clustering.
- K-Means: Geometric intuition, Centroids.
- K-Means: Mathematical formulation: Objective function
- K-Means Algorithm.
- How to initialize: K-Means++
- Failure cases/Limitations.
- K-Medoids
- Determining the right K.
- Code Samples.
- Time and Space complexity
- Unsupervised learning - Clustering - Hierarchical clustering Technique
- Agglomerative & Divisive, Dendrograms
- Agglomerative Clustering.
- Proximity methods: Advantages and Limitations.
- Time and Space Complexity.
- Limitations of Hierarchical Clustering.
- Code sample.
- Unsupervised learning - Clustering - DBSCAN (Density based clustering)
- Density based clustering
- MinPts and Eps:Density
- Core, Border and Noise points.
- Density edge and Density connected points.
- DBSCAN Algorithm.
- Hyper Parameters: MinPts and Eps.
- Advantages and Limitations of DBSCAN.
- Time and Space Complexity.
- Association Rule Mining (Apriori)
- Understanding of Support
- Understanding of Confidence
- Understanding of Lift
- Time and Space complexity
- Recommender Systems and Matrix Factorization
- Problem formulation: Movie reviews.
- Content based vs Collaborative Filtering.
- Similarity based Algorithms.
- Matrix Factorization: PCA,SVD.
- Matrix Factorization: NMF.
- Matrix Factorization for Collaborative filtering
- Matrix Factorization for feature engineering.
- Clustering as MF.
- Hyperparameter tuning.
- Matrix Factorization for recommender systems: Netflix Prize Solution.
- Cold Start problem.
- Word Vectors as MF.
- Eigen-Faces.
- Dimensionality reduction and Visualization
- What is dimensionality reduction?
- Row vector, and Column vector.
- How to represent a dataset?
- How to represent a dataset as a Matrix.
- Data preprocessing: Feature Normalization
- Mean of a datamatrix.
- Data preprocessing: Column Standardization
- Co-variance of a Data Matrix.
- MNIST dataset (784 dimensional)
- Code to load MNIST dataset.
- Principal Component Analysis.
- Geometric intuition.
- Mathematical objective function.
- Alternative formulation of PCA: distance minimization
- Eigenvalues and eigenvectors.
- PCA for dimensionality reduction and visualization.
- Visualize MNIST dataset.
- Limitations of PCA
- PCA for dimensionality reduction (not-visualization)
- T-distributed stochastic neighborhood embedding(t-SNE)
- What is -SNE?
- Neighborhood of a point, Embedding.
- Geometric intuition.
- Crowding problem.
- How to apply t-SNE and interpret its output(distill.pub)
- t-SNE on MNIST.
Module 6: Deep Learning and AI (Self Paced)
- Introduction to Python
- Introduction to Logistic Regression
- Introduction to Artificial Neural Network
- History of Neural networks and Deep Learning.
- How Biological Neurons work?
- Growth of biological neural networks.
- Diagrammatic representation: Logistic Regression and Perceptron.
- Multi-Layered Perceptron (MLP).
- Notation.
- Training a single-neuron model.
- Training an MLP: Chain Rule.
- Training an MLP:Memoization.
- Backpropagation.
- Activation functions.
- Vanishing Gradient problem.
- Bias-Variance tradeoff.
- Deep Multi-layer perceptrons
- Deep Multi-layer perceptrons:1980s to 2010s
- Dropout layers & Regularization.
- Rectified Linear Units (ReLU).
- Weight initialization.
- Batch Normalization.
- Optimizers:Hill-descent analogy in 2D
- Optimizers:Hill descent in 3D and contours.
- SGD Recap
- Batch SGD with momentum.
- Nesterov Accelerated Gradient (NAG)
- Optimizers:AdaGrad
- Optimizers : Adadelta andRMSProp
- Adam
- Which algorithm to choose when?
- Gradient Checking and clipping
- Softmax and Cross-entropy for multi-class classification.
- How to train a Deep MLP?
- Convolutional Neural Network
- Biological inspiration: Visual Cortex
- Convolution:Edge Detection on images.
- Convolution:Padding and strides
- Convolution over RGB images.
- Convolutional layer.
- Max-pooling.
- CNN Training: Optimization
- Receptive Fields and Effective Receptive Fields
- ImageNet dataset.
- Data Augmentation.
- Convolution Layers in Keras
- AlexNet
- VGGNet
- Recurrent Neural Network
- Why RNNs?
- Recurrent Neural Network
- Training RNNs: Backprop
- Types of RNNs
- Need for LSTM/GRU
- LSTM
- GRUs
Module 7: Power BI (Self Paced)
- Introduction to PowerBI and its architecture
- Import data from CSV files
- Import data from Excel files
- Import data from Web 1
- Import data from Web 2
- Import Real-time Streaming Data
- Import data from Oracle
- Import Data from Folder
- Dataflows - Introduction
- Dataflows - Create Gateway from Scratch
- Dataflows - Create Entities from CSV file
- Dataflows - Create Entities Using SQL Server
- Remove Rows
- Remove Columns
- Make first row as headers
- How to create calculate columns
- How to remove duplicates
- Unpivot columns and split columns
- Change Data type, Replace Values and Rearrange the columns
- Append Queries
- Merge Queries
- Visuals Intro
- Visuals-Bar Charts
- Visuals-Line Charts
- Visuals-Pie Chart
- Stacked bar Chart
- Clustered Column Chart
- Visuals-Area Chart and Analytics Tab Explained-0
- Visuals-Area Chart and Analytics Tab Explained-1
- Visuals-Combo Chart
- Visuals-Scatter Chart
- Visuals-Treemap Chart
- Visuals-funnel Chart
- Visuals-Card and Multi-Row Card
- Visuals-Gauge Card
- Visuals-KPIs
- Visuals-Matrix
- Visuals-Table
- Visuals-Text boxes - Shapes - Images
- Visuals-Slicers
- Visuals-Maps
- Custom Visuals - Word Cloud
- Visualization interactions
- Modeling and Relationships
- Other ways to create Relationship
- OLTP vs OLAP
- Star Schema vs Snowflake Schema
- DAX101 - Importing Data for Dax Learning
- DAX101 - Resources for Dax Learning
- DAX101 - What is Dax
- DAX101 - Dax Data Types
- DAX101 - Dax Operators and Syntax
- DAX101 - M vs Dax
- DAX101 - Create a Column
- DAX101 - Rules to Create Measures
- DAX101 - Calculated Columns vs Calculated Measures-0
- DAX101 - Calculated Columns vs Calculated Measures-1
- DAX101 - Sum()
- DAX101 - AVERAGE()-MIN()-MAX()
- DAX101 - SUMX()
- DAX101 - DIVIDE()
- DAX101 - COUNT()-COUNTROWS()
- DAX101 - CALCULATE()-0
- DAX101 - CALCULATE()-1
- DAX101 - FILTER()
- DAX101 - ALL()
- DAX101 - Time Intelligence - Create Date Table in M (important)
- DAX101 - Time Intelligence - Create Date Table in DAX
- DAX101 - Time Intelligence - SAMEPERIODLASTYEAR()
- DAX101 - Time Intelligence - TOTALYTD()
- DAX101 - Display Last Refresh Date
- DAX101 - Time Intelligence - PREVIOUSMONTH()
- DAX101 - Time Intelligence - DATEADD()
- DAX101 - Quick Measures
- PowerBI Reports
- PowerBI Workspaces
- PowerBI Datasets
- What are Dashboards
- How to create Workspace and Publish Report
- Favorite dashboards, reports, and apps in Power BI
- Subscribe to a Report or Dashboard
- Rename Workspace or Report or Dashboard
- Display Reports or Dashboards in Full screen mode
- Delete Reports or Dashboards
- Dashboard Menus
- File and View Options
- Printing Dashboard and Reports
- PATH Function
- PATHCONTAINS Function
- PATHITEM Function
- PATHITEMREVERSE Function
- PATHLENGTH Function
- RLS - Static Row Level Security
- RLS - Dynamic Row Level Security
- RLS - Organizational Hierarchy
- Sharing and Collaboration
- Sharing Dashboard
- Sharing Workspaces
- Sharing App
- Publish To Web
Program Fees
For Indian Residents
Rs. 140000 + GST*
Tenure
Deadline
Amount (INR)
1st instalment
Immediate Payment
Rs. 25000 + GST*
2nd Instalment
20 January 2023
Rs. 23000 + GST*
3rd Instalment
20 February 2023
Rs. 23000 + GST*
4th instalment
20 March 2023
Rs. 23000 + GST*
5th instalment
20 April 2023
Rs. 23000 + GST*
6th instalment
20 May 2023
Rs. 23000 + GST*
* Fee payable after Successful Placement
Rs. 40000 + GST*
For International Students
USD 5500
1st Installment will Start One Month Before the Commencement Date. NO COST EMI at 8850/-* (24 Months) (*NO COST EMI Subject to Approval)
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* 1% + GST* processing fees will be charged by loan partner in 6/9/12 months EMI.
* 1% + GST* processing fees will be charged by loan partner in 6/9/12 months EMI.
100% Job Placement
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Course Certificate
Data Science Sample Certificate
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Salary Graph Data Scientist
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Tools to be Used
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Learn from the best
Mr Rahul Tiwary
14+ Yr Exp. Professionals | Ex Deloitte | Ex Accenture | Ex Tech Mahindra
Mr Rakesh Sharma
14+ Yr Experience Professionals
Mr Rajesh Kumar
AVP, HDFC Ergo , 13+ yr Experience Professional
Deepak Arora
AVP at Barclays | 11+Yr Experience Professional
Ritu Shukla
Sr. Data Analyst ( 8 + Yr Experienced Professional)
Mr. Muzzammil
2+ Yr Exp. in Python, ML Few names in Industry who got 100% as appraisal & many recognition by his E
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- Average salary for the is Rs. 9.25 Lakhs per annum
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- Multiple offer letters for all our successful candidates
- Real time industry case studies with end to end implementation
- Multiple assignments for revision
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- Connect with The Scholar Alumini for network building
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