Data sciences are used to get meaningful insights from the large amount of structured and un-structured data. These Data are coming from various sources. Data are considered as an asset to an organization if its used effectively and efficiently. Beforehand storage part was the main focus, but the focus has now shifted towards processing and analysing the data. Data can be turned into valuable resources to create new strategies for Business and IT. It uses theories and techniques from many other fields like mathematics, computer science, Information Technology and statistics. But Data science has different functions than Information science and computer science. Predictive Analytics, decision making, Planning, Risk Detection, Recommendation, recognition, Security, all are the advantages of data sciences.
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MATLAB Fundamentals
Prerequisites: Undergraduate-level mathematics and experience with basic computer operations
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Deep Learning with MATLAB
MATLAB Fundamentals, Deep Learning Onramp
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Machine Learning with MATLAB
Prerequisites: MATLAB Fundamentals
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Predictive Modelling with Python
- 1. Introduction to Predictive Modelling
- 1.1 Concept of model in analytics and how it is used
- 1.2 Common terminology used in modelling process
- 1.3 Types of Business problems – Mapping of Algorithms
- 1.4 Different Phases of Predictive Modelling
- 1.5 Data Exploration for modelling
- 1.6 Exploring the data and identifying any problems with the data (Data Audit Report)
- 1.7 Identify missing/Outliers in the data
- 1.8 Visualize the data trends and patterns
- 2. Regression Problems
- 2.1 Linear Regression
- 2.2 Non-linear Regression
- 2.3 K-Nearest Neighbour
- 2.4 Decision Trees
- 2.5 Ensemble Learning – Bagging, Random Forest, Ad boost, Gradient Boost, XGBoost
- 2.6 Support Vector Regressor
- 3. Classification Problems
- 3.1 Logistic Regression
- 3.2 K-Nearest Neighbour
- 3.3 Naïve Bayes Classifier
- 3.4 Decision Trees
- 3.5 Ensemble Learning – Bagging, Random Forest, Ad boost, Gradient Boost, XGBoost
- 3.6 Support Vector Classifier
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Python for Data Science
Python, which once was considered a general programming language, has emerged as a shining star of the Data Science world. The key driver is the flexibility it offers for an end-to-end enterprise-wide analytics implementation, including machine learning and AI. No wonder Python for data science has become the industry's preferred choice; hence investing in a comprehensive data science python course becomes important for any aspirant.
- 1. Introduction to Basic Statistics
- Introduction to Statistics
- Measures of central tendencies
- Measures of variance
- Measures of frequency
- Measures of Rank
- Basics of Probability, distributions
- Conditional Probability (Bayes Theorem)
- 2. Introduction to Analytics & Data Science
- What are analytics & Data Science?
- Business Analytics vs. Data Analytics vs. Data Science
- Common Terms in Analytics
- Analytics vs. Data warehousing, OLAP, MIS Reporting
- Types of data (Structured vs. Unstructured vs. Semi Structured)
- Relevance of Analytics in industry and need of the hour
- Critical success drivers
- Overview of analytics tools & their popularity
- Analytics Methodology & problem-solving framework
- Stages of Analytics
- 3. Introduction to Mathematical Foundations
- Introduction to Linear Algebra
- Introduction to Calculus
- Theory of optimization
- 4. Visualising Geospatial data
- Introduction to Folium
- Maps with Markers
- Choropleth Maps
- 5. Operations with NumPy (Numerical Python)
- What is NumPy?
- Overview of functions & methods in NumPy
- Data structures in NumPy
- Creating arrays and initializing
- Reading arrays from files
- Special initializing functions
- Slicing and indexing
- Reshaping arrays
- Combining arrays
- NumPy Maths
- 6. Python Essentials (Core)
- Overview of Python- Starting with Python
- Why Python for data science?
- Anaconda vs. python
- Introduction to installation of Python
- Introduction to Python IDE’s (Jupyter, /Ipython)
- Concept of Packages – Important packages
- NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc
- Installing & loading Packages & Name Spaces
- Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
- List and Dictionary Comprehensions
- Variable & Value Labels – Date & Time Values
- Basic Operations – Mathematical/string/date
- Control flow & conditional statements
- Debugging & Code profiling
- Python Built-in Functions (Text, numeric, date, utility functions)
- User defined functions – Lambda functions
- Concept of apply functions
- Python – Objects – OOPs concepts
- How to create & call class and modules?
- 7. Overview of Pandas
- What are pandas, its functions & methods
- Pandas Data Structures (Series & Data Frames)
- Creating Data Structures (Data import – reading into pandas)
- 8. Cleansing Data with Python
- Understand the data
- Sub Setting / Filtering / Slicing Data
- Mutation of table (Adding/deleting columns)
- Binning data (Binning numerical variables into categorical variables)
- Renaming columns or rows
- Sorting (by data/values, index) -By one column or multiple columns – Ascending or Descending
- Type conversions
- Setting index
- Handling duplicates /missing/Outliers
- Creating dummies from categorical data (using get dummies ())
- Applying functions to all the variables in a data frame (broadcasting)
- Data manipulation tools (Operators, Functions, Packages, control structures, Loops, arrays etc.)
- 9. Data Analysis using Python
- Exploratory data analysis
- Descriptive statistics, Frequency Tables, and summarization
- Uni-variate Analysis (Distribution of data & Graphical Analysis)
- Bi-Variate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
- 10. Data Visualization with Python
- Introduction to Data Visualization
- Introduction to Matplotlib
- Basic Plotting with Matplotlib
- Line Plots
- 11. Visualisation Tools
- Basic Visualization Tools
- Advanced Visualization Tools
- 12. Statistical Methods & Hypothesis Testing
- Descriptive vs. Inferential Statistics
- What is probability distribution?
- Important distributions (discrete & continuous distributions)
- Deep dive of normal distributions and properties
- Concept of sampling & types of sampling
- Concept of standard error and central limit theorem
- Concept of Hypothesis Testing
- Statistical Methods – Z/t-tests (One sample, independent, paired), ANOVA, Correlation and Chi- square
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Basic Programming in R & Python
Course Objective: This course is designed for learners of Python. Emphasis will be placed on programming. Students of this course should have a basic knowledge of plotting, manipulating data, iterative processing, creating functions, applying functions on applications like excel or SAS. Prerequisites: Any programming background is appreciated but not mandatory. Learning Objectives: ➢ Install Python Software and packages ➢ import external forms of data ➢ data manipulation ➢ do iterative processing and simulate new data ➢ understand Python Functions
- Basic Operations in R
- ➢ Setting Directories
- ➢ Installation of R-Studio,
- 1Introduction to Python
- ➢ Installation of Python
- ➢ Packages in Python, Installing Packages,
- 2.Basic Operations in Python
- ➢ Programming Language Basics,
- ➢ Numbers, Strings Lists, Dictionaries, Tuples Files,
- ➢ Exercise/Case Study
- 3.Data Manipulation in Python
- ➢ Conditional Processing,
- ➢ Loops, Iterations, and other iterative processing,
- ➢ Exercise/Case Study
- ➢ Functions, arguments, and modules in Python
- ➢ Transforming Variables, Exercise/Case Study
- Overview of Python Packages/Libraries
- ➢ Popular Python Packages/Libraries
- ➢ Overview of Python application in analytics industry
- Analytics with R-Software
- Objective:
- Prerequisites
- Learning Objectives:
- Introduction to R
- ➢ Installation of R-Studio,
- ➢ Packages in R, Installing Packages,
- ➢ Setting Directories
- Basic Operations in R
- ➢ Programming Language Basics,
- ➢ Scalars, Vectors, Simple Calculations Data Structure,
- ➢ Data Frames, Exercise/Case Study
- Data manipulation in R
- ➢ Data Acquisition (Import & Export),
- ➢ Sub-setting observations, Subsetting variables,
- ➢ Transforming Variables, Renaming and Recoding Variables, Exercise/Case Study
- Data Manipulation in R
- ➢ Conditional Processing,
- ➢ Missing Values, Merging and Concatenating Datasets,
- ➢ Exercise/Case Study
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Tensor Flow
Course Overview This training also provides two real-time projects to sharpen your skills and knowledge and clear the TensorFlow Certification Exam. Course Duration: 30 Hours of Sessions 20 Hours of Labs
- 1. Introduction To Deep Learning
- Deep Learning: A revolution in Artificial Intelligence
- Limitations of Machine Learning
- Discuss the idea behind Deep Learning
- Advantage of Deep Learning over Machine learning
- 3 Reasons to go Deep
- Real-Life use cases of Deep Learning
- Scenarios where Deep Learning is applicable
- 1.1 The Math behind Machine Learning: Linear Algebra
- Scalars
- Vectors
- Matrices
- Tensors
- Hyperplanes
- 1.2The Math Behind Machine Learning: Statistics
- Probability
- Conditional Probabilities
- Posterior Probability
- Distributions
- Samples vs Population
- Resampling Methods
- Selection Bias
- Likelihood
- 1.3Review of Machine Learning Algorithms
- Regression
- Classification
- Clustering
- 2. Reinforcement Learning
- Underfitting and Overfitting
- Optimization
- Convex Optimization
- 3. Fundamentals Of Neural Networks
- Defining Neural Networks
- The Biological Neuron
- The Perceptron
- Multi-Layer Feed-Forward Networks
- Training Neural Networks
- Backpropagation Learning
- Gradient Descent
- Stochastic Gradient Descent
- Quasi-Newton Optimization Methods
- Generative vs Discriminative Models
- 3.1Activation Functions
- Linear
- Sigmoid
- Tanh
- Hard Tanh
- Softmax
- Rectified Linear
- Loss Functions
- Loss Function Notation
- Loss Functions for Regression
- Loss Functions for Classification
- Loss Functions for Reconstruction
- Hyperparameters
- Learning Rate
- Regularization
- Momentum
- Sparsity
- 4. Fundamentals Of Deep Networks
- Defining Deep Learning
- Defining Deep Networks
- Common Architectural Principals of Deep Networks
- Reinforcement Learning application in Deep Networks
- Parameters
- Layers
- Activation Functions – Sigmoid, Tanh, ReLU
- Loss Functions
- Optimization Algorithms
- Hyperparameters
- Summary
- 5. Introduction To TensorFlow
- What is TensorFlow?
- Use of TensorFlow in Deep Learning
- Working of TensorFlow
- How to install Tensorflow
- HelloWorld with TensorFlow
- Running a Machine learning algorithms on TensorFlow
- 6. Convolutional Neural Networks (CNN)
- Introduction to CNNs
- CNNs Application
- Architecture of a CNN
- Convolution and Pooling layers in a CNN
- Understanding and visualizing a CNN
- Transfer Learning and Fine-tuning Convolutional Neural Networks
- 7. Recurrent Neural Networks (RNN)
- Introduction to RNN Model
- Application use cases of RNN
- Modelling sequences
- Training RNNs with Backpropagation
- Long Short-Term memory (LSTM)
- Recursive Neural Tensor Network Theory
- Recurrent Neural Network Model
- 8. Restricted Boltzmann Machine (RBM) And Autoencoders
- Restricted Boltzmann Machine
- Applications of RBM
- Collaborative Filtering with RBM
- Introduction to Autoencoders
- Autoencoders applications
- Understanding Autoencoders
- Variational Autoencoders
- Deep Belief Network
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Data Robot Training
Requirements Experience with data analytics Familiarity with machine learning Audience Data scientists Data analysts
- Overview
- 1. Introduction
- 2. Overview of Data Robot Features and Architecture
- 3. Setting up a Data Robot Account
- 4. Preparing and Loading Data
- 5. Analysing Datasets
- 6. Modelling with Data Robot
- 7. Beginning the Modelling Process
- 8. Streamlining Model Development with Data Robot
- 9. Evaluating Results of Automated Modelling
- 10. Interpreting Models and Text Features
- 11. Generating Model Documentation
- 12. Making Predictions from Datasets
- 13. Deploying Models Built in Data Robot
- 14. Monitoring and Managing Deployed Models
- 15. Integrating Data Robot in Production
- 16. Managing Data Robot Projects
- 17. Summary and Conclusion
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Base SAS Programmer Certification
This course is for users who want to learn how to write SAS programs. It is the entry point to learning SAS programming and is a prerequisite to many other SAS courses. This course helps you prepare you for the following certification exam(s): SAS Certified Specialist: Base Programming Using SAS 9.4 Students should be familiar with enhancements and new functionality that are available in SAS 9.4.
- Learn how to & Who should attend
- Prerequisites
- Base SAS Programming
- SAS Programming 1: Essentials
- Course Overview
- Learn how to
- 1. Essentials
- The SAS programming process.
- Using SAS programming tools.
- Understanding SAS syntax.
- 2. Accessing Data
- Understanding SAS data.
- Accessing data through libraries.
- Importing data into SAS.
- 3. Exploring and Validating Data
- Exploring data.
- Filtering rows.
- Formatting columns.
- Sorting data and removing duplicates.
- 4. Preparing Data
- Reading and filtering data.
- Computing new columns.
- Conditional processing.
- 5. Analysing and Reporting on Data
- Enhancing reports with titles, footnotes, and labels.
- Creating frequency reports.
- Creating summary statistics reports.
- 6. Exporting Results
- Exporting data.
- Exporting reports.
- 7. Using SQL in SAS
- Using Structured Query Language in SAS.
- Joining tables using SQL in SAS
- SAS Programming 2: Data Manipulation Techniques
- Course Overview
- Learn how to & who should attend
- Prerequisites
- 1. Controlling DATA Step Processing
- a. Setting up for this course.
- b. Understanding DATA step processing.
- c. Directing DATA step output.
- 2. Summarizing Data
- a. Creating an accumulating column.
- b. Processing data in groups.
- 3. Manipulating Data with Functions
- a. Understanding SAS functions and CALL routines.
- b. Using numeric and date functions.
- c. Using character functions.
- d. Using special functions to convert column type.
- 4. Creating Custom Formats
- a. Creating and using custom formats.
- b. Creating custom formats from tables.
- 5. Combining Tables
- a. Concatenating tables.
- b. Merging tables.
- c. Identifying matching and nonmatching rows.
- 6. Processing Repetitive Code
- a. Using iterative DO loops.
- b. Using conditional DO loops.
- 7. Restructuring Tables
- a. Restructuring data with the DATA step.
- b. Restructuring data with the TRANSPOSE procedure.
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Advanced SAS Programmer Certification
This course is for SAS programmers who prepare data for analysis. The comparisons of manipulation techniques and resource cost benefits are designed to help programmers choose the most appropriate technique for their data situation. It focuses on the components of the SAS macro facility and how to design, write, and debug macro systems. Emphasis is placed on understanding how programs with and without macro code are processed. It also covers how to process SAS data using Structured Query Language (SQL).
- Learn how to & Who should attend?
- Prerequisites
- Advanced SAS Programming
- Duration
- Learn how to & Who should attend
- Prerequisites
- 1. Introduction
- a. Why SAS macro?
- b. Setting up for this course.
- 2. SAS Macro Facility
- a. Program flow.
- b. Creating and using macro variables.
- 3. Storing and Processing Text
- a. Macro functions.
- b. Using SQL to create macro variables.
- c. Using the DATA step to create macro variables.
- d. Indirect references to macro variables.
- 4. Working with Macro Programs
- a. Defining and calling a macro.
- b. Macro variable scope.
- c. Conditional processing.
- d. Iterative processing.
- 5. Developing Macro Applications
- a. Storing macros.
- b. Generating data-dependent code.
- c. Validating parameters and documenting macros.
- SAS SQL 1: Essentials
- Duration
- Learn how to & who should attend
- Prerequisites
- 1. Essentials
- 2. PROC SQL Fundamentals
- a. Subsetting data.
- b. Presenting data.
- c. Summarizing data.
- d. Creating and managing tables.
- e. Using DICTIONARY tables.
- 3. SQL Joins
- a. Introduction to SQL joins.
- b. Inner joins.
- c. Outer joins.
- d. Complex SQL joins.
- 4. Subqueries
- a. Noncorrelated subqueries.
- b. Correlated subqueries.
- c. In-line views.
- d. Creating views with the SQL procedure.
- e. Subqueries in the SELECT clause.
- f. Remerging summary statistics.
- 5. Set Operators
- a. Introduction to set operators
- b. The INTERSECT operator.
- c. The EXCEPT operator.
- d. The UNION operator.
- e. The OUTER UNION operator.
- 6. Using and Creating Macro Variables in SQL
- a. Interfacing PROC SQL with the macro language.
- b. Creating data-driven macro variables with a query.
- c. Using macro variables in SQL.
- 7. Accessing DBMS Data with SAS/ACCESS
- a. Overview of SAS/ACCESS technology.
- b. SQL pass-through facility.
- c. SAS/ACCESS LIBNAME statement.
- d. PROC FedSQL.
- SAS Programming 3: Advanced Techniques and Efficiencies
- Duration
- Learn how to & Who should attend
- 1. Getting Started
- a. Setting up for this course.
- b. DATA step review.
- 2. Using Advanced Functions
- a. Using a variety of advanced functions.
- b. Performing pattern matching with Perl regular expressions.
- 3. Defining and Processing Arrays
- a. Defining and referencing one-dimensional arrays.
- b. Doing more with one-dimensional arrays.
- c. Defining and referencing two-dimensional arrays.
- 4. Defining and Processing Hash Objects
- a. Declaring hash objects.
- b. Defining hash objects.
- c. Finding key values in a hash object.
- d. Writing a hash object to a table.
- e. Using hash iterator objects.
- 5. Using Utility Procedures
- a. Creating picture formats with the FORMAT procedure.
- b. Creating functions with the FCMP procedure.
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