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DATA SCIENCE - AI and Advanced Analytics

Insight: The Certified DATA SCIENCE PROGRAM in AI and Advanced Analytics aims to bring you the best available resources in the form of tools and techniques, along with hands on experience you need to succeed as a Data/ Business Analytics professional.
This program covers tools like SQL, Python, Informatica and Power BI and covers all techniques like Statistics and Exploratory Data Analysis. The program also covers Predictive modeling and concepts of Machine Learning.
More importantly, the program helps you prepare your Resume, prepares you for Analytics Role Interviews and provides one on one mentorship during the program.

Who is this Program For?
Engineers, Software and IT Professionals, Students and working professionals looking to enter the world of Analytics and Data Science

Job Opportunities: Data Analyst, Data Scientist, Product Analyst, Machine Learning Engineer, Business Analyst, Camera Algorithmic Engineer, Intelligent Economist; Culinary Product Developer; Cognitive Business Consultant; Digital Health Specialist

Duration Live Virtual Classroom Project/Assignment Assessment
693 Hours 240 Hours 278 Hours 58 Hours

Learning Path

Detailed Contents Outcome
1) RDBMS Concepts
2) Date Functions
3) Creating Views
4) Creating and Managing Tables
5) SQL Functions
6) Select Clause Data Retrival
7) Data Manipulation Language
8) JOINS
9) Set Operators
10) Transaction Control Language
11) Aggregates
12) Subqueries Correlated Queries
13) Conversion Functions
14) String Function
15) Normalization
16) SQL Assignments
1) Can perform basic SQL operations to store and retrieve data.
2) Maintain Data Integrity Rules.
3) Building Nested Queries, Apply Filters.
4) Joining Tables, perform order by and group by operations.
5) Create Functions, Views, Stored Procedures

Detailed Contents Outcome
1) Introduction to Data Visualization
2) Reporting in BI
3) Introduction to Power BI
4) Power BI Architecture
5) Building Blocks of Power BI
6) Visualization Analytics
7) Power BI Desktop Elements
8) Connect to the Different Data Sources
9) Query Editor – Data Cleansing & Transformation
10) Dashboarding
11) Modelling
12) DAX Formula
13) Visuals in Power BI
14) Customization of Visuals
15) Conditional Formatting Values
16) Bookmarks
17) Power BI Service
18) Workspace or Groups
19) Dashboards
20) Security
21) Scheduling
22) Exercises
1) Connect to various data sources and create interactive charts.
2) Create Dashboards that provide deeper business insights to take strategic decisions.
3) Build Business Story using Enterprise tools like Tableau.

Detailed Contents Outcome
1) Introduction to Python
2) Working with files
3) Connecting to database
4) Data wrangling
5) Exercise
1) Create Python Scripts to retrieve and manipulate data.
2) Perform Data Wrangling to restructure data.
3) Write user defined functions in Python to handle scenarios.

Detailed Contents Outcome
1) Introduction to the Cloud with AWS
2) Understand cloud computing
3) S3
4) Redshift,
5) RDS PostgreSQL
6) ETL process to extract data from S3 into Redshift
7) Exercise
1) Understand the advantages of Cloud Computing over traditional deployment models.
2) Ability to connect to Redshift and retrieve data using Python Script.
3) Understand the Pipeline through which data flows from Ingestion to Reporting.

Detailed Contents Outcome
1) Statistics Fundementals / Statistics Basics
2) Rules and Plots
3) Measures of Central Tendency, Variation
4) Sampling and Probability
5) Bayes Theorem
6) Discrete Probability Distribution
7) Continuous Probability Distribution
8) Confidence Interval, Central Limit Theorem
9) Hypothesis testing
10) ANOVA
11) ANOVA Demo and Exercises
12) Times Series
13) Data Clustering Regression
1) Ability to apply Statistical measures in ML.
2) Clear understanding of Probability and various types.
3) Ability to understand data distribution and its properties.
4) In-depth knowledge of Statistical Tests used in ML.

Detailed Contents Outcome
1) What is ML
2) Categories of Algorithm
3) Steps in Supervised Learning - Data Preperation-Feature Engineering-Model Building-Performance Measurement-Performance Improvement
4) Algorithm Complexity
5) Solutions to increase Performance
6) Decision Tree
7) Random Forest
8) Regression Model - Detailed Analysis
9) Naive Bayes
10) Support Vector Machine (SVM)
1) Knowledge of Supervised Learning Concepts.
2) Understanding the approach to build Supervised Model.
3) Clear understanding on how various algorithms such as Linear Regression, Decision Tree, Random Forest, Naive Bayes, Support Vector Machine works.
4) Ability to handle underfitting and overfitting models.
5) Ability to translate Model Outcome to solve business problems.

Detailed Contents Outcome
1) Clustering
2) Types of Clustering
3) K-Means
4) Applications of Clustering
5) Gaussian Mixture Model
6) Principal Component Analysis
7) Discriminant Analysis - LDA and MDA
8) Eigen Value and Eigen Vectors
9) Spectral Decomposition
10) Singular Value Decomposition
11) Anomaly Detection
12) Co- Variance Matrix
13) Time Series Data Analysis
1) Knowledge of Unsupervised Learning Concepts.
2) Understanding the approach to build Supervised Model.
3) Clear understanding on how Clustering works.
4) Ability to handle overfitting models by Principal Component Analysis.
5) Ability to translate Model Outcome to solve business problems.

Detailed Contents Outcome
1) Recommendation Engine
2) Text Analytics
3) Web Scraping
4) Natural Language Processing
5) EXPERT Systems
6) Artificial Neural Network (ANN)
1) Ability to handle unstructured data.
2) Perform Opinion Mining on Customer review/feedback and give insights to business.
3) Build Recommendation Engine.
4) Understand Deep Learning algorithms to work on Images.
5) Ability to analyze images/videos and perform Object Detection.

Detailed Contents Outcome
1) Project kickoff
2) Execution
3) Delivery
4) Evaluation
5) Presentation
Showcase your end-to-end understanding to the world!
Enroll Now

DATA ENGINEERING - AI and Advanced Analytics

Insight: DATA ENGINEERING is the fundamental need for a sustainable and strong foundation to an Analytics and Data Science career. Each Learner in this program will be able to apply basic skills and treat data mathematically. The Learner will be able to analyze large and complex data sets from multiple sources to provide insights and validate hypothesis. The Learner will also gain an in-depth understanding of the usage of Python and implementation of algorithms to mine targeted data and develop the ability to convert the data into a business story. The ability to analyze the information, identify patterns and trends to build effective dashboards is an important skill acquired as a part of the certification program.

Who is this Program For?
Engineers, Software and IT Professionals, Students and working professionals looking to enter the world of Analytics and Data Science

Job Opportunities: Data Analyst, Data Scientist, Product Analyst, Machine Learning Engineer, Business Analyst, Camera Algorithmic Engineer, Intelligent Economist; Culinary Product Developer; Cognitive Business Consultant; Digital Health Specialist

Duration Live Virtual Classroom Project/Assignment Assessment
440 Hours 240 Hours 160 Hours 28 Hours

Learning Path

Detailed Contents Outcome
SQL Write SQL commands to perform Create, Read, Update, and Delete commands
PL\SQL
Data Warehouse Concepts Use advanced SQL techniques to combine multiple datasets into one to create comprehensive databases
Data Analysis
ETL Tool Use ETL process (Extract, Transform, Load) to
transform and consolidate data from multiple
sources
Testing & QA
Exercises

Detailed Contents Outcome
Introduction to Python Create Python-based scripts to automate the cleanup, re-structuring, and rendering of large, heterogeneous datasets.
Working with files
Connecting to database
Data wrangling
Exercises

Detailed Contents Outcome
Introduction to the Cloud with AWS Understand Data Migration Process from On- Premise environment to Cloud.
Measure and Improve Efficiency and Performance.
Use Amazon Web Services (AWS) tools to create a Data Lake in the Cloud
Understand cloud computing
S3
Redshift, RDS PostgreSQL
ETL process to extract data from S3 into Redshift
Exercises

Detailed Contents Outcome
1) Introduction to Data Visualization
2) Reporting in BI
3) Introduction to Power BI
4) Power BI Architecture
5) Building Blocks of Power BI
Communicate and glean new business insights using enterprise-grade tools like Power BI
1) Visualization Analytics
2) Power BI Desktop Elements
3) Connect to the Different Data Sources
4) Query Editor – Data Cleansing & Transformation
1) Dashboarding
2) Modelling
3) DAX Formula
4) Visuals in Power BI
5) Customization of Visuals
6) Conditional Formatting Values
7) Bookmarks
8) Power BI Service
9) Workspace or Groups
10) Dashboards
11) Security
12) Scheduling
Use data to create visually exciting, interactive and informative Dashboards
Exercises

Detailed Contents Outcome
1) Project kickoff
2) Execution
3) Delivery
4) Evaluation
5) Presentation
Showcase your end-to-end understanding to the world!
Enroll Now

INTEGRATED DATA SCIENCE - AI and Advanced Analytics

Insight: Master the concepts necessary to cover all aspects of Data Science techniques real employers are looking for namely: Python, Applying advanced statistical techniques in Python, Data mining Techniques, Data Visualisation, Machine Learning and Deep Learning. Learn to excel and kick-start your career in Artificial Intelligence with the focus on practical understanding and application. At the end, you’ll be given a final project to apply what you’ve learned!

CAIA has made a consistent effort to create the most effective, time-efficient, and structured data science training available online, named the Integrated Data Science Course 2020.

We believe this is the first training program that solves the biggest challenge to entering the data science field – having all the necessary resources in one place. We have the best team of instructors comprising of Data Scientists from Systech Solutions leading you through real life use cases of their own clients. You also get to work closely with them on real time datasets with genuine clients

Moreover, our focus is to teach topics that flow smoothly and complement each other. The course teaches you everything you need to know to become a data scientist at a fraction of the cost of traditional programs.

Who is this Program For?
Engineers, Software and IT Professionals, Students and working professionals looking to enter the world of Analytics and Data Science

Job Opportunities: Data Analyst, Data Scientist, Product Analyst, Machine Learning Engineer, Business Analyst, Camera Algorithmic Engineer, Intelligent Economist; Culinary Product Developer; Cognitive Business Consultant; Digital Health Specialist

Duration Live Virtual Classroom Project/Assignment Assessment
150 Hours 80 Hours 50 Hours 20 Hours

Learning Path

Description: SQL is a must-have skill for every analytics professional. This course will start from the basics of databases and structured query language (SQL) and teach you everything you would need in any business/data analytics profession

Detailed Contents Outcome
1) RDBMS Concepts
2) Date Functions
3) Creating Views
4) Creating and Managing Tables
5) SQL Functions
6) Select Clause Data Retrival
7) Data Manipulation Language
8) JOINS
9) Set Operators
10) Transaction Control Language
11) Aggregates
12) Subqueries Correlated Queries
13) Conversion Functions
14) String Function
15) Normalization
16) SQL Assignments
1) Can perform basic SQL operations to store and retrieve data.
2) Maintain Data Integrity Rules.
3) Building Nested Queries, Apply Filters.
4) Joining Tables, perform order by and group by operations.
5) Create Functions, Views, Stored Procedures"

Description :Statistics forms the backbone of business analytics. Here, you will learn both descriptive as well as inferential statistics, how to use it for performing exploratory data analysis, and how all of that fits into the analytics world

Detailed Contents Outcome
1) Statistics Fundementals / Statistics Basics
2) Rules and Plots
3) Measures of Central Tendency, Variation
4) Sampling and Probability
5) Bayes Theorem
6) Discrete Probability Distribution
7) Continuous Probability Distribution
8) Confidence Interval, Central Limit Theorem
9) Hypothesis testing
10) ANOVA
11) ANOVA Demo and Exercises
12) Times Series
13) Data Clustering Regression
14) Scenario Based Excercises on Applied Statistics and Final Certification Exam
1) Apply statistical tools and concepts like moving average, hypothesis testing, ANOVA, and regression to data sets.
2) Understand the tools and techniques used in business analytics planning and monitoring

Description :Learn the most widely used programming language for business analytics. This course will teach you Python from scratch, including how to use it for data cleaning, exploration and a whole host of tasks you’ll perform as an analyst. Learn time series analysis and build your first time series forecasting model using time series forecasting methods in Python for a real-life industry use case in association with an Analytics organisation Partner.

Detailed Contents Outcome
1) Python Programming Fundamentals /Python Basics
2) File Handling, Exception Handling and Database connection in Python
3) Data Wrangling and Cleanup of Data using Python
4) Mathematical Computing using Python - NumPy
5) Data Manipulation with Pandas
6) Usage of Python Package Libraries - NumPy, Pandas, Matplotlib, Scikit- Learn
7) Beginner's Guide to learning Web scraping with Python
8) Django - Web Development with Python Django Framework - Tutorial
9) Designing a Chatbot using Python
10) Tensor Flow and Keras
11) Scenario Based Excercises and Final Certification Exam
Python Course will help you master important Python programming concepts such as data operations, file operations, object-oriented programming and various Python libraries such as Pandas, Numpy, Matplotlib which are essential for Data Science

Description : Learn the basics of core machine learning algorithms like Linear Regression, Logistic Regression, and Decision Trees. Machine Learning forms the crux of Business Decision making for many organisations across the world. Learn how organisation use various algorithms to build their Machine learning model to convert data into insights and decisions for the future.

Detailed Contents Outcome
1) Python
2) Advanced Statistics
3) Supervised Learning
4) Unsupervised Learning
5) Recommendation system
6) Text Analytics
7) NLP and Deep Learning
8) Simulation Project and Final Certification Exam
Python Course will help you master important Python programming concepts such as data operations, file operations, object-oriented programming and various Python libraries such as Pandas, Numpy, Matplotlib which are essential for Data Science

Description: Whether you are creating dashboards for your business customers or solving cutting-edge business analytics problems, structured thinking and communication is a must-have skill for every data professional. Convert your data into actionable insights, create dashboards to impress your clients, and learn Power BI tips, tricks and best practices for your analytics/ Data visualisation role

Detailed Contents Outcome
1) Introduction to Data Visualization
2) Reporting in BI
3) Introduction to Power BI
4) Power BI Architecture
5) Building Blocks of Power BI Visualization Analytics
6) Power BI Desktop Elements
7) Connect to the Different Data Sources
8) Query Editor – Data Cleansing & Transformation Dashboarding
9) Modelling
10) DAX Formula
11) Visuals in Power BI
12) Customization of Visuals
13) Conditional Formatting Values
14) Bookmarks
15) Power BI Service
20) Workspace or Groups
21) Dashboards
22) Security
23) Scheduling
24) Exercise
25) Creating Dashboards using Power BI - Simulation Project
1)You will learn Power BI concepts like Microsoft Power BI Desktop layouts, BI reports, dashboards, Power BI DAX commands and functions.
2) You will explore to experiment, fix, prepare and present data quickly and easily.
Enroll Now