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.
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1.1 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
1.2. 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
1.3. 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
1.4. 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
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2.1. 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
2.2. 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
2.3. 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
2.4. 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
2.5. Create parameters to enable interactivity
2.5.1. In calculations
2.5.2. With filters
2.5.3. With reference lines
2.6. Structure the data
2.6.1. Sets
2.6.2. Bins
2.6.3. Hierarchies
2.6.4. Groups
2.7. 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)
2.8. 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
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3.1. 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)
3.2. 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
3.3. 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)
3.4. Format dashboards
3.4.1. Apply color, font, shapes, styling3.4.2. 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
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4.1. 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
4.2. Schedule data updates
4.2.1. Schedule data extract refreshes
4.2.2. Schedule a Tableau Prep workflow
4.3. Manage Published workbooks
4.3.1. Create alerts
4.3.2. Create subscriptions
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
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