Data analysis 360 (Advanced Excel, MS SQL, Python, PowerBI,TABLEAU,Base SAS)
Earn certification, gain practical skills, and advance your career with job placement assistance and networking opportunities. Join our vibrant learning community and stay ahead in the world of data-driven decision making
Description
This comprehensive course is designed to equip participants with the essential skills and knowledge required to excel in the field of Data Analysis and programming. From foundational concepts to advanced techniques, participants will learn how to leverage various tools and programming languages to analyze data, derive insights, and make informed decisions.
Throughout the course, participants will delve into the fundamentals of Data Analysis, gaining hands-on experience with industry-leading tools and programming languages such as Python, SQL, and Tableau. From data manipulation and visualization to machine learning and big data analysis, this course covers a wide array of topics to equip participants with the skills and knowledge needed to excel in the rapidly evolving field of Data Analysis.
Whether you're a beginner looking to break into the field or a seasoned professional aiming to upskill, Jagan will help you achieve your goals and unlock your full potential in the world of Data Analysis and programming.
What you'll learn
Master essential Data Analysis techniques for informed decision-making.
Gain proficiency in Python programming for data manipulation and analysis.
Understand SQL fundamentals for database querying and management.
Build & test a full website design.
Create impactful visualizations using Tableau for data-driven storytelling.
Explore advanced statistical analysis and hypothesis testing methods.
Develop machine learning models for predictive analytics and pattern recognition.
Dive into big data technologies like Hadoop for scalable data processing.
Apply data cleaning and preprocessing techniques to ensure data quality.
Utilize clustering and classification algorithms for data segmentation and categorization.
Learn data visualization best practices to effectively communicate insights.
Prepare for a rewarding career in Data Analysis, business intelligence, or data science.
Requirements
- No prior programming experience required, but a willingness to learn programming concepts is beneficial.
- Basic understanding of mathematics and statistics concepts.
- Commitment to continuous learning and self-improvement in the field of Data Analysis and programming.
Course Content
Learn from industry experts with years of practical experience in data analysis, programming, and visualization.
- SQL INTRO (data, TYPES OF DATA, Database and relational database, ssms, Tsql overview)
- Sublanguages, constraints
- Operators
- Special operators
- Schemas, order by, where conditions
- Group by, Having, Roll up, Cube, Distinct
- Functions
- Joins
- Ranking functions
- Views and synonyms
- Sub Queries
- TCL Commands
- CTE
- Duplicates
- ER Modelling, Normalization
- Variables in SQL
- Control statements
- User defined function
- Triggers
- Stored procedures
- Excel Overview
- Ribbon, Quick Access Toolbar, Mini Toolbar.
- Excel shortcut keys
- Cell referencing style, relative cell reference and absolute cell reference
- Data Validation
- Introduction to the Data and Data Formats.
- Copy, Cut, Paste, Hide, Unhide, Link the Data in Rows, Columns and Sheet.
- Inserting, Deleting, Moving, and linking the data in between the multiple sheets.
- Text functions
- Logical functions
- Datetime functions
- Conditional formatting
- Lookup function
- Pivot table and pivot charts
- Charts and dashboards
Power Bi Introduction, Components of power bi, History and architecture, software installation
Power Query
Extraction of data from different sources, GUIs, Exploring all menu bars and options
Data Types and Filters
Column and row transformation using GUIs
Merging and appending queries
Transform, Add Column, viewtab and tooltab Power Pivot
Data modelling - Dimension table and Fact table
data modelling -2 (relationship active and inactive)
cross filter direction, assume referential integrity, apply security filters. DAX
Dax introduction - new column, new measure, quick measure, new table
Text functions
Date and time function
Logical functions
Date dimension table
Mathematical functions
Filter function
Time intelligence function
Power View
Power view intro
Visual interaction
Filters in power view
Drill down reports and hierarchy
Different types of Visuals - purpose, formatting options and fieldwells
Sorting, grouping and binning
Bookmarks, selection panes and Buttons Power Bi Service
Power Bi Service Introduction, workspaces, dataset preparation, creating reports
Dashboard
Data gateways
Dataflows
Sharing reports and dashboards, Team collaboration, Row Level Security
Incremental Refresh
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Connect to Data Sources
1.1.1. Choose an appropriate data source
1.1.2. Choose between live connection or extract
1.1.3. Connect to extracts
1.1.4. Connect to spreadsheets
1.1.5. Connect to .hyper files (or .tde files)
1.1.6. Connect to relational databases
1.1.7. Pull data from relational databases by using custom SQL queries
1.1.8.Connect to a data source on Tableau Server
1.1.9. Replace the connected data source with another data source for an existing chart or sheet Prepare data for analysis
1.2.1. Assess data quality (completeness, consistency, accuracy)
1.2.2. Perform cleaning operations
1.2.3. Organize data into folders
1.2.4. Use multiple data sources (establish relationships, create joins, union tables, blend data)
1.2.5. Prepare data by using Data Interpreter, pivot, and split
1.2.6. Create extract filters Perform data transformation in Tableau Prep
1.3.1. Choose which data transformation to perform based on a business scenario
1.3.2. Combine data by using unions
1.3.3. Combine data by using joins
1.3.4. Shape data by using aggregations
1.3.5. Perform filtering
1.3.6. Shape data by using pivots
Customize fields
1.4.1.Change default field properties (types, sorting, etc.)
1.4.2. Rename columns
1.4.3. Choose when to convert between discrete and continuous
1.4.4. Choose when to convert between dimension and measure
1.4.5. Create aliases
Create calculated fields
2.1.1. Write date calculations (DATEPARSE, DATENAME…)
2.1.2. Write string functions
2.1.3. Write logical and Boolean expressions (If, case, nested, etc.)
2.1.4. Write number functions
2.1.5. Write type conversion functions
2.1.6. Write aggregate functions
2.1.7. Write FIXED LOD calculations
Create quick table calculations
2.2.1. Moving average
2.2.2. Percent of total
2.2.3. Running total
2.2.4. Difference and percent of difference
2.2.5. Percentile
2.2.6. Compound growth rate
Create custom table calculations
2.3.1. Year to date
2.3.2. Month to date
2.3.3. Year over year
2.3.4. Index
2.3.5. Ranking
2.3.6. First-last
Create and use filters
2.4.1. Apply filters to dimensions and measures
2.4.2. Configure filter settings including Top N, Bottom N, include, exclude, wildcard, and conditional
2.4.3. Add filters to context
2.4.4. Apply filters to multiple sheets and data sources
Create parameters to enable interactivity
2.5.1. In calculations
2.5.2. With filters
2.5.3. With reference lines Structure the data
2.6.1. Sets
2.6.2. Bins
2.6.3. Hierarchies
2.6.4. Groups
Map data geographically
2.7.1. Create symbol maps
2.7.2. Create heat maps
2.7.3. Create density maps
2.7.4. Create choropleth maps (filled maps)
Summarize, model, and customize data by using the Analytics feature
2.8.1. Totals and subtotals
2.8.2. Reference lines
2.8.3. Reference bands
2.8.4. Average lines
2.8.5. Trend lines
2.8.6. Distribution bands
2.8.7. Forecast by using default settings
2.8.8. Customize a data forecasting model
2.8.9. Create a predictive model
Create charts
3.1.1. Create basic charts from scratch (bar, line, pie, highlight table, scatter plot, histogram, tree map, bubbles, data tables, Gantt, box plots, area, dual axis, combo)
3.1.2. Sort data (including custom sort) Create dashboards and stories
3.2.1. Combine sheets into a dashboard by using containers and layout options
3.2.2. Add objects
3.2.3. Create stories
Add interactivity to dashboards
3.3.1. Apply a filter to a view
3.3.2. Add filter, URL, and highlight actions
3.3.3. Swap sheets by using parameters or sheet selector
3.3.4. Add navigation buttons
3.3.5. Implement user guiding sentences (click…, hover…, menu options) Format dashboards
3.4.1. Apply color, font, shapes, styling
3.4.2. Add custom shapes and color palettes
3.4.3. Add annotations
3.4.4. Add tooltips
3.4.5. Apply padding
3.4.6. Remove gridlines, row-level and column-level bands, and shading
3.4.7. Apply responsive design for specific device layouts Publish Content
4.1.1. Publish a workbook
4.1.2. Publish a data source
4.1.3. Print content
4.1.4. Export content
Schedule data updates
4.2.1. Schedule data extract refreshes
4.2.2. Schedule a Tableau Prep workflow
Manage Published workbooks
4.3.1. Create alerts
4.3.2. Create subscriptions
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Overview of Python
Python in data analysis
Python Editors & IDE's(Jupyter notebook) Concept of Packages - Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc.) Python Data Types
Type Conversion
Data Object types
Conditional Logic, Logical Operators, For Loop, Iterables, range(), enumerate() While Loops
break, continue, pass functions
Modules In python
Web Scraping with Python
Control flow & conditional statements
User defined functions – Lambda functions
Concept of apply functions
Python – Objects – OOPs concepts
How to create classes and modules?
How to call classes and modules?
Concept of pipelines in Python
- NumPy - Data structures in NumPy, arrays
- Slicing and indexing
- Reshaping arrays
- Combining arrays with Hstack, Vstack
- Data manipulation with numpy
- Pandas Data Structures (Series & Data Frames)
- pandas, its functions & methods
- Creating Data Structures (Data import – reading into pandas)
- Importing Data from various sources (Csv, txt, excel,etc.)
- Exploring the data
- Filtering Data
- Dropping rows & columns
- Adding/deleting columns
- Binning data
- Encoding
- Renaming columns or rows
- Sorting
- Identifying duplicates
- Missing values treatment
- Capping outliers
- One-hot encoding
- Descriptive statistics in python
- Inferential statistics in python
- Probability distribution
- Sampling and population
- Central limit theorem
- Confidence interval
- Hypothesis testing
- Matplotlib
Instructor
Jagananathan
Data Analytica & SAS Expert Trainer
Jagan's passion for teaching and commitment to student success make him a standout instructor at JS Analytics. His ability to simplify complex concepts, coupled with his patient demeanor and hands-on teaching style, ensures that students feel supported every step of the way. Whether you're a beginner looking to break into the field or a seasoned professional aiming to upskill, Jagan will help you achieve your goals and unlock your full potential in the world of Data Analysis and programming.
Over the years, he expanded his expertise to include programming languages such as SAS and Python, mastering the art of data manipulation and machine learning algorithms.
Agalya 7 Days ago
The best Data Nalytics Course
"As someone with a background in programming, I was eager to learn Python for data analysis. I enrolled in JS Analytics' Data Analysis using Python course, and it was everything I hoped for and more. The instructors were experts in their field, and the course content was comprehensive yet easy to follow. The hands-on projects were particularly enjoyable and allowed me to apply what I learned in real-world scenarios. I can't recommend this course enough to fellow Python enthusiasts!"
Shakivel 3 Days ago
The best Data analysis Course
"JS Analytics' Advanced Excel course was exactly what I needed to take my Excel skills to the next level. The course covered advanced formulas, functions, and data analysis techniques that have proven invaluable in my day-to-day work. The instructors were patient and provided personalized feedback, and the course materials were well-structured. Whether you're a beginner or an experienced Excel user, this course is sure to enhance your skills and productivity."