Data Analyst Course in Gandhinagar
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Enroll in our advanced Data Analytics training program in Gandhinagar and achieve proficiency in just 3 months. Enhance your skills with cutting-edge techniques and receive expert mentorship for a successful career journey.
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- On site or virtual based sessions
- Customised Course
- Curriculum with industry relevant use cases
- Pre & Post assessment service
- Complimentary basic Courses
- Corporate based learning management system with team and individual dashboard and reports
Data Analytics Course Overview
360DigiTMG offers the best Data Analytics certification courses in Gandhinagar. The training program equips you with an apt understanding of data processing tools like Excel, SQL/NoSQL, and Data Visualization tools like Tableau and Power BI. While SQL/NoSQL is used to work with the data stored in the Database Management software, Tableau and Power BI are used in analyzing it and presenting visual stories to end-users. Concepts such as Data Preparation, Data Cleansing, and Exploratory Data Analysis are explored in detail. Influential concepts like Data Mining of Structured (RDBMS) and Unstructured (Big Data) data, with the aid of real-life examples, are illustrated. Advanced Excel aids in data proficiency Concepts and it will help to reduce reduces working hours.
1. What is Power BI?
Power BI is the perfect tool to convert unrelated sources of data into coherent, visually immersive, and interactive insights. It uses a collection of software services, connectors, and apps to get the work done. Whether your data be an Excel spreadsheet or a collection of cloud-based and on-premises hybrid data warehouses. With Power BI it gets easy to connect to different data sources, visualize and understand what is vital, and share. Power BI has numerous elements that all work together, but the below three points are the basic ones.
- Power BI Desktop: As the name suggests is a Power BI application for Windows Desktop.
- Power BI Service: An online SaaS service
- Power BI Mobiles apps: This is an app special for Windows, iOS, and Android devices.
2. What is Tableau?
Tableau is a very famous and most used Data Visualization tool, and it helps in understanding the trends, insights, and patterns to other connections in Dataset. Its major task is to connect and extract the data from different stored places. Tableau specializes in pulling data from any platform or database. After the initial launch of Tableau, the ready data connectors will allow you to connect to any database. Data extracted by the above process will be connected to the data engine, Tableau desktop. Dashboards are created by Data Analysts or Data Engineer’s using the extracted data and are shared with users in the form of static files. With the help of the Tableau Server, the end-user can access files from all locations.
3. What is No SQL?
Not only SQL (No SQL) is a form of non-tabular databases that store data differently than traditional data storing databases. The databases change with respective data models. Key-value, wide-column, and graph are popular documents. It provides flexible schemas and scales easily with bigger datasets. No SQL database is used actively with
- Fast-paced agile development
- Storage of structured and semi-structured data
- Huge volumes of data
- Requirements of scale-out architecture
- Application paradigms like microservices and real-time streaming
4. What is SQL?
If you are dealing with Relational Databases, then SQL is the standard language. It can be used to insert, update, delete and search database records. We can also optimize and maintain the databases with the help of SQL. There are five types of SQL queries that are widely used: Data Definition Language (DDL), Data Manipulation Language (DML), Data Control Language (DCL), Transaction Control Language (TCL), Data Query Language (DQL) With the help of SQL users can:
- Access data in RBDMS systems
- Describing data
- It gives you access to manipulate specific data in the database.
- Creating databases and tables
- You can monitor who can use, see and alter the tables.
5. What is Advanced Excel?
Knowledge in Excel means processing the ability to use spreadsheets, calculations, automation, and tables efficiently to process huge data for businesses. Advance Excel will make this task easier by solving complex things in Excel. For example, it can manage high amounts of data using advanced level functions and excel features like power query, advanced data filters, asap features, power map. Advance Excel aids in data proficiency and reduces working hours.
Learning Outcomes of Data Analytics Training in Gandhinagar
The main aim of the course is to give you an outlook towards the various techniques used in handling huge data sets via Data Analytics. Learners get to assess the applications of these technologies that are used in storing and processing huge amounts of data. This module instructs the student on the various techniques used to analyze structured and unstructured data, building visual stories using Tableau and or Power BI capabilities. The Data Analytics course is the ideal course for professionals who want to acquire in-depth knowledge of daily used Data frameworks. The three-month Data Analytics training in Meerut will cover the essential tools like SQL, NoSQL, Tableau, Power BI, and Advanced Excel concepts. Students will learn to store, retrieve, manipulate, and analyze large datasets stored in Database management systems like relational database management systems or document-based database systems. They will also be introduced to various concepts to represent the data on the serving layer to show results in easier and readily consumable visual formats. The course contains multiple applied case studies that enable the participants to solve complex business problems improving profitability in their companies.
Data Analyst Course Syllabus in Gandhinagar
- Introduction to Python Programming
- Installation of Python & Associated Packages
- Graphical User Interface
- Installation of Anaconda Python
- Setting Up Python Environment
- Data Types
- Operators in Python
- Arithmetic operators
- Relational operators
- Logical operators
- Assignment operators
- Bitwise operators
- Membership operators
- Identity operators
- Data structures
- Vectors
- Matrix
- Arrays
- Lists
- Tuple
- Sets
- String Representation
- Arithmetic Operators
- Boolean Values
- Dictionary
- Conditional Statements
- if statement
- if - else statement
- if - elif statement
- Nest if-else
- Multiple if
- Switch
- Loops
- While loop
- For loop
- Range()
- Iterator and generator Introduction
- For – else
- Break
- Functions
- Purpose of a function
- Defining a function
- Calling a function
- Function parameter passing
- Formal arguments
- Actual arguments
- Positional arguments
- Keyword arguments
- Variable arguments
- Variable keyword arguments
- Use-Case *args, **kwargs
- Function call stack
- Locals()
- Globals()
- Stackframe
- Modules
- Python Code Files
- Importing functions from another file
- __name__: Preventing unwanted code execution
- Importing from a folder
- Folders Vs Packages
- __init__.py
- Namespace
- __all__
- Import *
- Recursive imports
- File Handling
- Exception Handling
- Regular expressions
- Oops concepts
- Classes and Objects
- Inheritance and Polymorphism
- Multi-Threading
- List Comprehensions
- List comprehension
- Dictionary comprehension
- Enumerate
- Zip and unzip
- Generator Expressions
- Tuples – Nested, Names, Unpacking
- Splitting – Slicing Objects, Ellipsis
- Augmented Assignments with Sequences
- Build-in Sort Functions
- Ordered Sequences with Bisect
- Arrays, Memory Views, Deques
- Handling Missing Keys
- Set Theory, Variations, Operations
- Higher-Order Functions
- Function Annotations
- Functional Programming Packages
- Procedural vs Functional
- Pure functions
- Map()
- Reduce()
- Filter()
- Lambdas
- Loop vs Comprehension vs Map
- Identify, Equality & References
- MySQL db Module
- INSERT, READ, DELETE, UPDATE, COMMIT, ROLLBACK operations on SQL using Python
- Python Packages
- Pandas – Series, Dataframes
- Numpy – Arrays, Memory, Matrices, Broadcasting, Masked Arrays
- Scipy
- Matplotlib
- Seaborn
- Sklearn (Scikit Learn)
- Statsmodels
- Jupyter Notebooks, IPython Notebooks
- Data Collection using CSV, JSON, XML, HTML & Scrapping
- Data Wrangling
- Understanding
- Filtering
- Typecasting
- Transformations & Normalization
- Imputation
- Handling Duplicates & Categorical Data
- Data Summarization
- Data Visualizations using Python Packages
- Line Chart
- Bar Chart
- Histogram
- Pie Charts
- Box Plots
- Scatter Plots
- Figures & Subplots
- Additional Visualization Packages – bokeh, ggplot, plotly
- Python XML and JSON parsers
- Basic Images Processing using Python OpenCV
- Dates and Times
- Binary Data
- Pythonic Programming
- Exception Handling
- Purpose of Exception Handling
- Try block
- Except block
- Else block
- Finally block
- Built-in exceptions
- Order of ‘except’ statements
- Exception - mother of all exceptions
- Writing Custom exceptions
- Stack Unwinding
- Enhancing Classes
- Metaprogramming
- Developer Tools
- Unit Testing with PyTest
- Multi-Threading
- Program Memory Layout
- Concurrency
- Parallelism
- Process
- Thread of execution
- Creating a thread
- Joining a thread
- Critical section
- Locks
- PyQt
- Network Programming
- Scripting for System Administration
- Serializing
- Advanced-Data Handling
- Implementing Concurrency
- Asynchronous programming
- The asyncio framework
- Reactive programming
- Parallel Processing
- Introduction to parallel programming
- Using multiple processes
- Parallel Cython with OpenMP
- Automatic parallelism
- Introduction to Concurrent and Parallel Programming
- Technical requirements
- What is concurrency?
- Not everything should be made concurrent
- The history, present, and future of concurrency
- A brief overview of mastering concurrency in Python
- Setting up your Python environment
- Django with REST Webservices
- Client-Server architecture
- Web Application
- Web framework
- Installing Django modules
- Creating first basic Django
- Creating Model classes
- Django Template tags and template programming
- Django rest framework
- Understanding REST Architecture
- HTTP GET, POST
- JSON serialization
- Writing REST API
- Web Extraction
- Beautiful Soup
- Selenium
- Serialization pickling, XML & JSON
- Introduction to Serialization
- Structure and Container
- Pickle Module
- pickling built-in data structures
- byte strings
- binary
- xml parsing and construction - xml
- json parsing and construction - json, simplejson
- Logging
- Purpose of logging
- Logging levels
- Types of logging
- Logging format
- Logging Handlers
- Disadvantages of excessive logging
- Custom loggers
- Eye for Detail - (Tableau Crosstabs), Highlight tables
- Comparative Analysis - Bar Graphs, Side-By-Side Bars, Circle Views, Heat Map, Bubble Chart
- Composition Analysis - Pie Chart, Donut Chart, Stacked Bar Graph
- Trend Analysis - Line Graphs and Area Graphs (Discrete and Continuous)
- Hierarchial Data Representation - Tree Map
- Correlation Analysis - Scatter Plot
- Distribution Analysis - Tableau Histogram, Box and Whisker Plot
- GeoSpatial Data Representation - Filled Maps, Symbol Maps, Combination Maps, Polygon Maps
- Relative comparison of 2 Measures - Bullet Graph, Dual Axis Chart, Dual Combination Chart, Blended Axis Chart, Bar in a Bar Chart
- Pareto Analysis - Pareto Chart
- Statistical Control Chart
- Tableau Gantt Chart
- Tableau Desktop Specialist
- Tableau Desktop Certified Associate
- Power BI Tips and Tricks & ChatGPT Prompts
- Overview of Power BI
- Architecture of Power BI
- Power BI and Plans
- Installation and introduction to Power BI
- Importing data
- Changing Database
- Data Types in Power BI
- Basic Transformations
- Managing Query Groups
- Splitting Columns
- Changing Data Types
- Working with Dates
- Removing and Reordering Columns
- Conditional Columns
- Custom columns
- Connecting to Files in a Folder
- Merge Queries
- Query Dependency View
- Transforming Less Structured Data
- Query Parameters
- Column profiling
- Query Performance Analytics
- M-Language
- Managing Data Relationships
- Data Cardinality
- Creating and Managing Hierarchies Using Calculated Tables
- Introduction to Visualization
- What is Dax?
- How to write DAX
- Types of Function in DAX
- Creating Calculated Measures
- Types of Application of DAX
- Introduction
- Pie and Doughnut charts
- Treemap
- Bar Chart with Line (Combo Chart)
- Filter (Including TopN)
- Slicer
- Focus Mode and See Data
- Table and Matrix
- Gauge, Card, and KPI
- Coloring Charts
- Shapes, Textboxes, and Images
- Gridlines and Snap to Grid
- Custom Power BI visuals
- Tooltips and Drilldown
- Page Layout and Formatting
- Visual Relationship
- Maps
- Python and R . Visual Integration
- Analytics Pane
- Bookmarks and Navigation
- Selection pane
- Overview of Dashboards and Service
- Uploading to Power BI Service
- Quick Insights
- Dashboard Settings
- Natural Language Queries
- Featured Questions
- Sharing a Dashboard
- In-Focus Mode
- Notifications and Alerts in the Power BI Service
- Personal Gateway Publishing to Web Admin Portal
- Introduction
- Creating a Content Pack
- Using a Content Pack
- Row Level Security
- Summary
- Accessing Looker Studio
- Connectors
- Creating a Report
- Controlling Data Access
- Editing Data Source Schema
- Other Common Data Source Operations
Alumni Speak
"The training was organised properly, and our instructor was extremely conceptually sound. I enjoyed the interview preparation, and 360DigiTMG is to credit for my successful placement.”
Pavan Satya
Senior Software Engineer
"Although data sciences is a complex field, the course made it seem quite straightforward to me. This course's readings and tests were fantastic. This teacher was really beneficial. This university offers a wealth of information."
Chetan Reddy
Data Scientist
"The course's material and infrastructure are reliable. The majority of the time, they keep an eye on us. They actually assisted me in getting a job. I appreciated their help with placement. Excellent institution.”
Santosh Kumar
Business Intelligence Analyst
"Numerous advantages of the course. Thank you especially to my mentors. It feels wonderful to finally get to work.”
Kadar Nagole
Data Scientist
"Excellent team and a good atmosphere. They truly did lead the way for me right away. My mentors are wonderful. The training materials are top-notch.”
Gowtham R
Data Engineer
"The instructors improved the sessions' interactivity and communicated well. The course has been fantastic.”
Wan Muhamad Taufik
Associate Data Scientist
"The instructors went above and beyond to allay our fears. They assigned us an enormous amount of work, including one very difficult live project. great location for studying.”
Venu Panjarla
AVP Technology
Why Choose 360DigiTMG for Data Analyst Training Institute in Gandhinagar?
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