Data Science Certification Course

Module 1

1. A-Z Python for data science Installation of python IDE’S
2. Python environment Setup.
3. Python data types: List, Tuple Set, Dictionary
4. Conditional statements
a. If
b. if-else.
c. Nested if
d. if elif ladder
5. Loops
a. for loop,
b. while loop
6. Functions
a. Custom functions
b. Inbuilt function.
7. OOP Concept
a. Class & object
b. Inheritance
c. Init method
8. Exception handling
a. Try
b. Except
c. Finally
d. Types of exceptions
9. File handling
a. Read
b. Write
c. Append

Module 2 – Statistics

1. Definition
2. inferential and descriptive statistics
3. Mean, median, mode
4. Variant, standard deviation, range, skewness, kurtosis, untitance and interval, z-distribution, tardancy, p-value, f-test, anova, chi-square test and masseuse of dispersion

Module 3 – Probability

1. Definition, types of probability (conditional probability, joint probability)
2. Random variables, probability distribution, bayer’s theorem
3. Linear algebra, eigen vectors & eigen values, maximize & minimize functions. Iift ratio, orthogonal matrix
4. Central limit theorem, hypothesis testing, power law
5. Correlation regression & covariance
6. Probability mass function, cumulative distribution function.

Module 4 – Numpy

1. Installation & introduction of NumPy package
2. NumPy basics
3. Creation of NumPy arrays
4. Array operations
5. Array slicing
6. Multidimensional array
7. Python list VS NumPy arrays
8. Basic linear algebra operations

Module 4 – Pandas

1. Installation and introduction of pandas package
2. Pandas basics
3. Indexing and Recording files
4. Data operations
5. Grouping, merging, joining & concatenating
6. Creating objects
7. Viewing data
8. Data selection
9. Data manipulation
10. Working with data & time
11. Working with different types of files ( CSV, Excel, Text file etc.)

Module 5 – Data Visualisation

1. Basics of visualisation
2. Installation of visualisation packages like matplotlib, seaborn etc.
3. Working with different types of plot / grapes
a. Scatter
b. Line chart
c. Bar chart
d. Histogram
e. Boxplot
f. a-a plot
g. Pie-chart etc

Module 6 – Data preparation / data cleaning / munging / wrangling

1. Outlier analysis / treatment
2. Missing value imputation
3. Data filtering
4. Typecasting
5. Transformations
6. Duplicate data handling
7. Categorical data handling
8. Discretization
9. Standardisation and normalisation of data
10. Zero & near zero variance features

Module 7 – Feature Engineering

1. Rounding
2. Binarization
3. Binning
4. Transformations
5. Feature engineering on text data
6. Feature scaling
7. Feature selection techniques

Module 1:-Basics

• Database Concepts
• E-R Modeling and Diagram
• Normalization
• SQL Server
• Introduction to SQL
• DDL and DML Statements
Module 2: Queries (DQL)
• Select Statement
• Top, Distinct, Null etc…Keywords
• String and Arithmetic Expressions
• Where Clause with Operators
• Sorting data using Order By clause, basic of Sub Queries
Module 3: Aggregate Functions
• functions in Queries
• predefined functions
• Group By with Rollup and Cube and Group By with Rollup and Cube
• Count, Sum, Min, Max, Avg Group By and Having Clause
Module 9: Joins and Set – Operations
• Introduction to Joins Cross Joins
• Inner Join, Outer Join, Self-Join
• Unions, Intersect and Except
• Implementation of Data integrity
Module 5: Constraints
• Unique
• Not NULL
• Primary Key
• Default Check Foreign Key
Module 6: Implementing Views
• Introduction & Advantages of Views
• Creating, Altering, Dropping Views, SQL Server Catalogue Views
Module 7: Extra – Features
• Pivot Table
• Common Table Expression
• Ranking Functions Using BLOB data type
• Using XML data type

Module 1 – Statistics

1. Definition
2. inferential and descriptive statistics
3. Mean, median, mode
4. Variant, standard deviation, range, skewness, kurtosis, untitance and interval, z-distribution, tardancy, p-value, f-test, anova, chi-square test and masseuse of dispersion

Module 2- Probability

1. Definition, types of probability (conditional probability, joint probability)
2. Random variables, probability distribution, bayer’s theorem
3. Linear algebra, eigen vectors & eigen values, maximize & minimize functions. Iift ratio, orthogonal matrix
4. Central limit theorem, hypothesis testing, power law
5. Correlation regression & covariance
6. Probability mass function, cumulative distribution function.

Module 1 – Numpy

1. Installation & introduction of NumPy package
2. NumPy basics
3. Creation of NumPy arrays
4. Array operations
5. Array slicing
6. Multidimensional array
7. Python list VS NumPy arrays
8. Basic linear algebra operations

Module 2 – Pandas

1. Installation and introduction of pandas package
2. Pandas basics
3. Indexing and Recording files
4. Data operations
5. Grouping, merging, joining & concatenating
6. Creating objects
7. Viewing data
8. Data selection
9. Data manipulation
10. Working with data & time
11. Working with different types of files ( CSV, Excel, Text file etc.)

Module 3 – Data Visualisation

1. Basics of visualisation
2. Installation of visualisation packages like matplotlib, seaborn etc.
3. Working with different types of plot / grapes
a. Scatter
b. Line chart
c. Bar chart
d. Histogram
e. Boxplot
f. a-a plot
g. Pie-chart etc

Module 4 – Data preparation / data cleaning / munging / wrangling

1. Outlier analysis / treatment2. Missing value imputation
3. Data filtering
4. Typecasting
5. Transformations
6. Duplicate data handling
7. Categorical data handling
8. Discretization
9. Standardisation and normalisation of data
10. Zero & near zero variance features
Module 9 – Feature Engineering
1. Rounding
2. Binarization
3. Binning
4. Transformations
5. Feature engineering on text data
6. Feature scaling
7. Feature selection techniques

Module 5 – machine learning algorithm

1. ML introduction
2. ML VS AI
3. AI VS ML VS DL
4. Different types of ML algorithm ( unsupervised & supervised )
Module 11 – Unsupervised learning
1. clustering
a. Hierarchical clustering
b. DB-Scan clustering
2. Dimension reduction
a. PCA
b. LDA
c. SVD
3. Association rule
a. Market basket analysis
b. Measure of association
c. Apriori algorithm

Module 6 – Supervised algorithm / regression

1. Regression analysis / SLR
a. Scatter diagram
b. Correlation causation
c. Correlation coefficient
d. Simple linear regression
2. Non-linear regression techniques
3. Model evaluation
a. Loss function
b. Cost function
c. Eraser function
4. Multiple linear regression
a. SLR VS MLR
b. Line assumption
c. Residuals and predicting variable plots
d. Influence plot
e. Feature selection
5. Lasso – ridge regression
a. Regularisation techniques
b. Over fitting and underfitting
c. Elastic net regression
6. Logistic regression
a. Confusion matrix
b. Performance matrix
c. ROC curve
d. AVC curve
7. Multi class regression
a. Multinomial regression
b. Ordinal logistic regression

Module 7- machine learning algorithms / classifications

1. KNN classification
a. Parametric learning
b. Bias variance frecte
c. K value
2. Decision tree
a. Elements of Decision tree
b. Greedy algorithm
c. Measure of entropy
d. Gini index, gain ratio
e. Information gain
f. Pruning technique
3. Ensemble techniques
a. Bagging & boosting
i. Voting
ii. Stacking
iii. Bootstrap aggregation
iv. Random forest IC fold validation
b. Adaboost & extreme gradient boosting
i. Adaptive boosting
ii. Reweighting
iii. Hyperparameter
iv. Cross validation
v. K fold CV
4. Naive bayes
a. Conditional probability
b. Naive – bayes classifier / Probabilistic classification
c. Prior probability
i. Data prior
ii. Class prior
iii. Marginal likelihood
d. Posterior probability
e. Text classification using naive bayes
Module 14 – Recommendation Engine
1. User based collaborative filtering
2. Content based filtering
3. SVD in recommendation

Module 1 – Text mining & natural language processing

1. Bag of words
2. Pre-processing
3. DTM & TDM
4. Stemming
5. Lemmatization
6. TF / TF – IDF
7. Word cloud
8. Corpus level word clouds
a. Sentimental analysis
b. Positive word clouds
c. Negative word cloud
d. Unigram, bigram, trigram
9. Latent Dirichlet Allocation (LDA)
10. Topic modelling
11. Parts of speech tagging
Module 16 – Network analytics
1. Definition of a network / graph
2. Vertices / nocks
3. Edges / connection / links
a. Adjacency matrix
b. Unidirectional
c. Bidirectional
4. Node properties
5. Network properties

Module 1 – time series / forecasting

1. Survival analytics
a. Duration analysis
b. Censoring
c. Survival, hazard, cumulative hazard functions
d. Kaplon – mier survival functional and curve
2. Introduction to time series
3. Steps to forecasting
4. Lagplot & ACF
5. Different errors in forecasting
6. Model based approaches
7. AR model for eraser
8. Data driven algorithms
9. ARIMA ( auto – registration integrated moving average )
10. Smoothing techniques

Module 1 – Deep learning basics

• Introduction to Biological & Artificial Neuron
• Mathematical foundation-DL
• Introduction to ANN,CNN and RNN
• Neuron, Weights, Activation function, Integration function, Bias and Output
• Introduction to Perceptron
• Multi-Layered Perceptron (MLP)
• Activation functions
1. Identity Function,
2. Step Function,
3. Ramp Function,
4. Sigmoid Function,
5. Tanh Function,
6. ReLU, ELU, Leaky ReLU & Maxout
• Back Propagation
• Weights Calculation in Back Propagation
Module 19 – ANN
• Error Surface, Learning Rate & Random Weight Initialization
• Local Minima in Gradient Descent Learning
• Gradient Primer, Activation Function, Error Function, Vanishing Gradient, Error Surface challenges, Learning Rate challenges, Decay Parameter, Gradient Descent Algorithmic Approaches, Momentum, Nestrov Momentum, Adam, Adagrad, Adadelta & RMSprop
• Overfitting, DropOut, DropConnect, Noise, Data Augmentation, Parameter Choices, Weights Initialization (Xavier, etc.)

Module 2 – CNN

• Parameters used in MLPs
• Convolution Networks
• Convolution Layers with Filters
• Pooling Layer, Padding, Stride
• Transfer Learning
• Weight decay, Drop Connect, Data Manipulation Techniques & Batch Normalization

Module 3 – RNN

• Introduction to Adversaries
• Language Models – Next Word Prediction, Spell Checkers, Mobile Auto-Correction, Speech Recognition & Machine Translation
• Traditional Language model
• Disadvantages of MLP
• Introduction to State & RNN cell
• Introduction to RNN
• RNN language Models
• Back Propagation Through time
• RNN Loss Computation
• Types of RNN
• Combining CNN and RNN for Image Captioning
• Architecture of CNN and RNN for Image Captioning
• Bidirectional RNN and Deep Bidirectional RNN
• Disadvantages of RNN
• Frequency-based Word Vectors
• Count Vectorization (Bag-of-Words, BoW), TF-IDF Vectorization
• Word Embeddings
• Word2Vec – CBOW & Skip-Gram
• FastText, GloVe

• Introduction
• Basic charts in tableau
• Organizing and simplifying Data
• Visual analytics
• Advanced analytics in tableau
• Maps in tableau
• Dashboards
• Stories
• Calculations
• LOD Expressions
• Custom charts

• AIOPS
• Github
• Dockers and Containers
• Auto AI
• Explainable AI
• Kafka
• CICD Pipeline
• MongoDB