Data Analytics/BI Developer Training in Tambaram Chennai

Greens Technologies in Tambaram is known for offering quality training programs in various IT domains, including data analytics. Here are some aspects that might make Greens Technologies a good choice for data analytics training.

What is Data Analytics?

Data Analytics is the process of examining, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making. It involves using various techniques and tools to analyze data and uncover patterns, trends, and relationships. Here are the key components and steps involved in data analytics:

1. Data Collection: Gathering data from various sources, such as databases, sensors, logs, and online repositories.
2. Data Cleaning: Identifying and correcting errors or inconsistencies in the data to ensure its accuracy and completeness.
3. Data Transformation: Converting data into a suitable format for analysis. This may include normalization, aggregation, and other preprocessing steps.
4. Data Exploration: Using statistical and visualization techniques to understand the data’s structure, distribution, and key characteristics.
5. Data Modeling: Applying mathematical models, algorithms, and machine learning techniques to analyze the data and make predictions or identify patterns.
6. Data Interpretation: Translating the results of the analysis into actionable insights, which can inform business decisions or scientific research.
7. Data Presentation: Communicating the findings through reports, dashboards, and visualizations to stakeholders in a clear and understandable manner.

Types of Data Analytics:

1. Descriptive Analytics: Summarizes past data to understand what has happened. Example: Monthly sales reports.
2. Diagnostic Analytics: Investigates the reasons behind past outcomes. Example: Analyzing customer complaints to find the root cause of issues.
3. Predictive Analytics: Uses historical data and statistical models to forecast future events. Example: Predicting future sales based on past trends.
4. Prescriptive Analytics: Suggests actions to achieve desired outcomes. Example: Recommending inventory levels to minimize stockouts.

Applications of Data Analytics:

• Business: Market analysis, customer segmentation, and financial forecasting.
• Healthcare: Patient diagnosis, treatment optimization, and health trend analysis.
• Sports: Performance analysis, strategy development, and injury prevention.
• Government: Policy making, resource allocation, and fraud detection.
• Science and Research: Hypothesis testing, experimental analysis, and data-driven discoveries.

Data analytics is a multidisciplinary field that combines elements of statistics, computer science, and domain expertise to turn raw data into valuable insights.

Our Services

Descriptive Analytics – Understand what has happened in your business with detailed reports and visualizations. Track performance metrics, identify trends, and make data-driven decisions.

Diagnostic Analytics – Dive deep into your data to uncover the reasons behind trends and outcomes. Identify root causes, pinpoint issues, and take corrective actions with confidence.

Predictive Analytics – Stay ahead of the curve by forecasting future trends and behaviors. Leverage advanced statistical models and machine learning to predict sales, customer behavior, and more.

Prescriptive Analytics – Make informed decisions with recommendations based on your data. Optimize your operations, improve resource allocation, and achieve your business goals efficiently. 

Industries We Serve

Business

Boost your market analysis, customer segmentation, and financial forecasting. Make strategic decisions with confidence.

Healthcare

Enhance patient outcomes, optimize treatment plans, and analyze health trends. Turn data into better healthcare solutions.

Retail

Improve inventory management, personalize customer experiences, and increase sales. Use data to drive your retail success.

Finance

Identify market trends, manage risks, and optimize investments. Leverage data for better financial planning. 

Manufacturing

Optimize production processes, improve quality control, and reduce downtime. Utilize data for smarter manufacturing.

How It Works

1. Consultation: We start with a detailed discussion to understand your needs and objectives.
2. Data Collection: We gather and clean your data to ensure accuracy and completeness.
3. Analysis: Our experts apply advanced techniques to analyze your data and uncover insights.
4. Reporting: We present our findings in clear, actionable reports and visualizations.
5. Implementation: We help you implement data-driven strategies to achieve your goals. 

Why should I choose data analytics?

Choosing data analytics as a career or field of study offers numerous benefits and opportunities, making it a compelling choice for many individuals. Here are several reasons why you should consider pursuing data analytics:

High Demand and Job Opportunities

1. Growing Field: Data analytics is a rapidly growing field with increasing demand across various industries, including finance, healthcare, retail, and technology.
2. Job Security: As organizations continue to collect vast amounts of data, the need for skilled data analysts to interpret and leverage this data is rising, leading to strong job security.
3. Diverse Roles: Data analytics offers a wide range of roles such as data analyst, data scientist, business analyst, and data engineer, allowing for diverse career paths.

Competitive Salaries

1. Attractive Compensation: Data analysts often earn competitive salaries, with potential for significant growth as you gain experience and expertise.
2. Performance Bonuses: Many organizations offer performance-based bonuses and incentives for data professionals who can drive business results.

Impactful Work

1. Informed Decision-Making: Data analysts play a crucial role in helping organizations make data-driven decisions, leading to better outcomes and improved efficiency.
2. Problem Solving: The field involves solving complex problems, providing innovative solutions, and contributing to strategic planning.

Skill Development

1. Technical Skills: Gain proficiency in various tools and technologies such as SQL, Python, R, Tableau, Power BI, and machine learning algorithms.
2. Analytical Thinking: Develop strong analytical and critical thinking skills, which are valuable in many professional and personal contexts.
3. Continuous Learning: The field of data analytics is constantly evolving, offering continuous learning opportunities and keeping your skills up-to-date.

Versatility and Applicability

1. Industry Versatility: Data analytics skills are applicable across a wide range of industries, providing flexibility to work in sectors that interest you.
2. Business Versatility: The skills you acquire in data analytics can be applied to various business functions, including marketing, operations, finance, and product development.

Collaborative Environment

1. Team Collaboration: Work closely with different teams, including marketing, finance, and IT, fostering a collaborative and interdisciplinary work environment.
2. Communication Skills: Enhance your ability to communicate complex data insights to non-technical stakeholders, improving your overall communication skills.

Future-Proof Career

1. Emerging Technologies: Stay at the forefront of technological advancements such as artificial intelligence, machine learning, and big data, ensuring your career remains relevant.
2. Adaptability: The skills and knowledge gained in data analytics make you adaptable to various roles and industries, providing career longevity.

Personal Satisfaction

1. Intellectual Challenge: Enjoy the intellectual challenge of working with data, uncovering patterns, and deriving meaningful insights.
2. Achievement: Experience a sense of accomplishment from turning raw data into actionable insights that drive real business value.

Topics Covered:

Course Name: Power BI

1. Tools: Microsoft Power BI

Topics 

1. M Query
2. Power Query Editor
3. Data types
4. Column Tracking
5. Split columns
6. Choose column
7. Keep / Remove Rows
8. Append Queries
9. Duplicates Queries
10. Creating Dim Table
11. Join Kind
12. Merge Queries
13. Join
14. Extract
15. Format
16. Functions
17. Course Name : Power BI
18. Tools : Microsoft Power BI
19. TABLEAU – POWER BI SYLLABUS
20. DATA ANALYTICS
21. TABLEAU-POWER BI-SQL-PYTHON
22. Data Modeling
23. DAX Functions
24. Data Types
25. Basic Functions
26. Date Function
27. Aggregation Functions
28. Logical Function
29. Filter Function
30. Time Intelligence Function
31. Information Function
32. POWER BI VISUALAZATION:
33. Dashboards
34. Formatting
35. POWER BI SERVICE 

Course Name: Tableau

2. Tools: Tableau

Topics 

1. Tableau interface demo
2. Tableau Products
3. Tableau Desktop
4. Data types in tableau
5. Types of Connection
6. Dimension and measures
7. Tableau file extension
8. Set, Parameter, Group
9. Bin, Hierarchy, Combine
10. Sorting, Types of Axis, Maps
11. Filters
12. Quick Table Calculation
13. Analytics Functions
14. Logical Functions
15. String Functions
16. Table Calculation Functions
17. Window Functions
18. Date Functions
19. LOD-Level of Detail
20. Types of Charts
21. Dashboard Functions
22. Dashboard Actions
23. Data Modeling
24. Tableau Server/Online
25. Embedded Data Source
26. Published Data Source
27. Different site roles
28. User controls
29. RLS (Row level Security)
30. Alert, Subscription, Metrics
31. Empty Extract
32. Real time Scenarios
33. Dashboard Creation From Scratch

Course Name: SQL Tools: Oracle Topics:

3. Tools: SQL

Topics 

1. Introduction to sql
2. Introduction to databases
3. Install a database engine
4. Data types
5. Operators
6. Case manipulation functions
7. Character manipulation functions
8. Date functions
9. Null functions
10. Number functions
11. General functions
12. Aggregate functions/group
13. functions
14. Analytical functions
15. Data definition language commands
16. Data manipulation language
17. commands
18. Transaction control language
19. commands

•  Data query language commands

20. Joins
21. Constraints
22. Set operators
23. View
24. Materialized view
25. Sub-queries
26. Pseudo columns 

Course Name: Python

4. Tools: Python

Topics:

1. Introduction
2. Literate Programming
3. Anaconda InstallatIon Procedure
4. Jupyter Notebook Environment
5. Python Hello World (Print statement in
6. python) Basics
7. Strings
8. Python Functions
9. Conditional Statements
10. Iteration
11. Lists
12. Tuples
13. Dictionaries
14. Packages and Modules
15. Regular Expressions
16. File Handling
17. File Data Processing
18. Sets
19. Functional Programming
20. Maps in python
21. Filters in Python
22. Lambdas in Python
23. Numpy
24. Pandas
25. Matplotib