Softlogic Systems Data Analytics Course Syllabus is specifically designed for College Students, Freshers, and Job Seekers. Our Data Analytics Syllabus covers the data analytics fundamentals, statistical analysis, data visualization, SQL, Excel, Python for analytics, and business intelligence tools. Our Data Analytics Course Content helps you learn Data Analytics Step by Step with real-time projects and Interview Preparations.
Data Analytics Course Syllabus
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Syllabus for The Data Analytics Course
CORE PYTHON
- Python Introduction & history
- Color coding schemes
- Salient features & flavors
- Application types
- Language components
- String handling management
- String operations – indexing, slicing, ranging
- String methods – concatenation, repetition, formatting
- Supporting functions
- Native data types
- List
- Tuple
- Set
- Dictionary
- Decision making statements
- If
- If…else
- If…elif…else
- Looping statements
- For loop
- While loop
- Function types
- Built-in functions
- Math functions
- User defined functions
- Recursive functions
- Lambda functions
- OOPs
- Classes and objects
- init constructor
- Self-keyword
- Data abstraction
- Data encapsulation
- Polymorphism
- Inheritance
- Exception handling
- Error vs exception
- Types of error
- User defined exception handling
- Exception handler components
- Try block, except block, finally block
POWER BI INTRODUCTION
- Data Visualization
- Reporting Business Intelligence (BI)
- Traditional BI
- Self-Serviced BI Cloud Based BI
- On Premise BI
- Power BI Products
- Power BI Desktop (Power Query, Power Pivot, Power View)
- Flow of Work in Power BI Desktop
- Power BI Report Server
- Power BI Service, Power BI Mobile
- Power BI Architecture
- A Brief History of Power BI
POWER QUERY
- Data Transformation
- Benefits of Data Transformation
- Shape or Transform Data using Power Query
- Overview of Power Query / Query Editor
- Query Editor User Interface
- The Ribbon (Home, Transform, Add Column, View Tabs)
- The Queries Pane
- The Data View / Results Pane
- The Query Settings Pane, Formula
- Bar Saving the Work
- Data types
- Changing the Data type of a Column Filters in Power Query
- Auto Filter / Basic Filtering Filter a Column using
- Text Filters Filter a Column using Number Filters
- Filter a Column using Date Filters Filter Multiple Columns
- Remove Columns / Remove Other Columns Name
- Rename a Column Reorder Columns or Sort Columns
- Add Column / Custom Column Split Columns Merge
- Columns PIVOT, UNPIVOT Columns Transpose Columns
- Header Row or Use First Row as Headers Keep Top Rows
- Keep Bottom Rows Keep Range of Rows Keep Duplicates
- Keep Errors Remove Top Rows
- Remove Bottom Rows
- Remove Alternative Rows
- Remove Duplicates, Remove Blank Rows
- Remove Errors Group Rows / Group By
M LANGUAGE
- IF..ELSE Conditions
- TransformColumn()
- RemoveColumns()
- SplitColumns()
- ReplaceValue()
- Table.Distinct() Options and GROUP BY Options
- Table.Group()
- Table.Sort() with Type Conversions
- PIVOT Operation and Table.Pivot ().
- List Functions Using Parameters with M Language
DATA MODELING
- Data Modeling Introduction Relationship
- Need of Relationship Relationship Types
- Cardinality in General
- One-to-One
- One-to-Many
- Many-to-One
- Many-to-Many
- AutoDetect the relationship
- Create a new relationship
- Edit existing relationships
- Make Relationship Active or Inactive
- Delete a relationship
DAX
- What is DAX
- Calculated Column, Measures
- DAX Table and Column Name Syntax
- Creating Calculated Columns
- Creating Measures
- Calculated Columns Vs Measures
- DAX Syntax & Operators
- Types of Operators
- Arithmetic Operators
- Comparison Operators
- Text Concatenation Operator
- Logical Operators
DAX FUNCTIONS TYPES
- Date and Time Functions
- YEAR, MONTH,DAY
- WEEKDAY, WEEKNUM FORMAT (Text Function)
- Month Name, Weekday Name
- IF
- TRUE, FALSE NOT,
- OR, IN, AND
- Text Function
- LEN, CONCATENATE
- LEFT, RIGHT, MID UPPER
- LOWER TRIM, SUBSTITUTE, BLANK
- Logical Functions
- IF TRUE, FALSE NOT
- OR, IN, AND IF ERROR SWITCH
- Math & Statistical Functions
- INT ROUND, ROUNDUP
- ROUNDDOWN
- DIVIDE EVEN, ODD
- POWER, SIGN SQRT
- FACT SUM, SUMX MIN, MINX MAX
- MAXX COUNT,
- COUNTX AVERAGE
- AVERAGEX COUNTROWS
- COUNTBLANK
REPORT VIEW
- Report View User Interface
- Fields Pane
- Visualizations pane
- Ribbon, Views, Pages Tab
- Canvas Visual Interactions Interaction Type (Filter, Highlight, None)
- Visual Interactions Default Behavior, Changing the Interaction
- Grouping and Binning Introduction
- Using grouping, Creating Groups on Text Columns
- Using binning, Creating Bins on Number Column and Date Columns
- Sorting Data in Visuals
- Changing the Sort Column
- Changing the Sort Order
- Sort using column that is not used in the Visualization
- Sort using the Sort by Column button
- Hierarchy Introduction
- Default Date Hierarchy
- Creating Hierarchy
- Creating Custom Date Hierarchy
- REPORT VIEW
- Change Hierarchy Levels
- Drill-Up and Drill-Down Reports
- Data Actions, Drill Down, Drill Up, Show Next Level
- Expand Next Level Drilling filters other visuals option
VISUALIZATIONS
- Visualizing Data
- Why Visualizations
- Visualization types
- Create and Format Bar and Column Charts
- Create and Format Stacked Bar Chart
- Stacked Column Chart
- Create and Format Clustered Bar Chart
- Clustered Column Chart
- Create and Format 100% Stacked Bar Chart 100% Stacked Column Chart
- Create and Format Pie and Donut Charts
- Create and Format Scatter Charts
- Create and Format Table Visual
- Matrix Visualization
- Line and Area Charts
- Create and Format Line Chart, Area Chart
- Stacked Area Chart Combo Charts
- VISUALIZATIONS
- Create and Format Line and Stacked Column Chart
- Line and Clustered Column Chart
- Create and Format Ribbon Chart
- Waterfall Chart, Funnel Chart
POWER BI SERVICE
- Power BI Service Introduction
- Power BI Cloud Architecture
- Creating Power BI Service Account
- SIGN IN to Power BI Service Account
- Publishing Reports to the Power BI service
- Import / Getting the Report to PBI Service
- My Workspace / App Workspaces Tabs
- DATASETS, WORKBOOKS, REPORTS & DASHBOARDS
- Working with Datasets Creating Reports in Cloud using Published
- Datasets
- Creating Dashboards Pin Visuals and Pin LIVE
- Report Pages to Dashboard
- Advantages of Dashboards Interacting with
- Dashboards
- Formatting Dashboard, Sharing Dashboard
ADVANCED PANDAS FUNCTIONS
- Group by()
- Pivot tables()
- Multi-indexing()
- merge()
- concatenate()
- join()
- data transformation using apply()
- map()
- query()
- Resampling time series functionality
- excel writer()
- pipe()
- creating dataframes
- reading CSV files with intrinsic index
- converting CSV files to dataframes
- converting dataframes to CSV files
- converting dataframes to excel file
ADVANCED SQL FUNCTIONS
- Common Table Expressions (CTE)
- Recursive CTE’s
- temporary functions
- pivoting data with sum() and CASE WHEN
- Except vs Not in
- self joins, rank vs dense_rank vs row number
- ranking data
- calculating delta values,
- multiple groupings using rollup
- calculating running totals
- computing a moving average
- date time manipulations
- Formatting strings, stored methods
- JOINS
- Sub Queries
- Manipulation of date and time
- procedural data storage
- Connecting SQL to Python or R language, window Functions
PROJECT
- Project1 – Product Sales Analysis – Power BI Project and review
- Project2 – Financial Performance Analysis – Power BI Project and review
- Project3 – Health care sales Analysis –
- Intermediate Power BI project and review
- Project4 – Anamoly detection in Credit card transactions – Intermediate Power BI project and review
Conclusion
The Data Analytics Course Syllabus above is for college students, people who have just graduated, and those looking for a job. Our Softlogic Systems provides a syllabus about Data Analytics, including data analytics fundamentals, statistical analysis, data visualization, SQL, Excel, Python for analytics, and business intelligence tools. After completing this syllabus, you will do projects, prepare for job interviews, and apply for jobs. By learning step by step, Data Analytics will help students get a job placement. The goal is to make students learn Data Analytics in a way that helps them get a job.
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How do descriptive, diagnostic, predictive, and prescriptive analytics differ from one another?
Descriptive analytics summarizes past data to provide insights into what happened. Diagnostic analytics investigates why something happened. Predictive analytics estimates future trends using historical data, while prescriptive analytics advises on actions to achieve particular objectives or results.
What is the role of data analytics?
Data is made sense of by individuals and organizations with the use of data analytics. Usually, data analysts examine unprocessed data to find patterns and insights. They assist firms in making decisions and succeeding by utilizing a variety of tools and strategies. To learn more, enroll in our data analytics online course.
What is the level of expertise for the trainers in SLA?
Our trainers in SLA are seasoned and experienced. They will teach students from base level to advanced level due to their years of experience.
What can I learn from this Data Analytics Training?
Through this Data Analytics Training you will learn the fundamentals of data science, machine learning, and artificial intelligence, as well as the art of communication within the data-driven organization. Additionally, the course will cover topics such as data collection, distribution, and visualization.
What methods do you use to address missing data in a dataset?
Missing data can be handled through various methods such as imputation (replacing missing values with statistical estimates), deletion (removing rows or columns with missing data), or using algorithms designed to handle missing values directly.
Is data analytics a good career?
All businesses have a high demand for data analysts, and those who wish to advance in their careers have many options. Data analysts are needed by many elite organizations to help them leverage their data more effectively or to give them the right insights to support their business operations.
What types of payment methods does SLA accept?
SLA does indeed accept a wide range of payment methods starting from cheques, cash, cards to all types of UPI digital payments.
Does SLA teach practicals?
Yes, SLA does definitely teach hands-on practical training as part of its curriculum.
What are the advantages of using machine learning algorithms in data analysis?
Machine learning algorithms can uncover complex patterns and relationships in large datasets, automate predictive modeling, and adapt to new data over time, providing more accurate and dynamic insights.
What are the benefits of Data Analytics?
Data analytics allows businesses to identify trends and relationships in data that were previously hidden. By using this data to improve decision making, organizations can make better use of resources, optimize operations, gain a competitive edge, and increase revenues and profits.
Is a data analyst an IT job?
Although working with IT tools and systems is necessary, a data analyst position is not always an IT (information technology) job.
What measures do you take to maintain data privacy and security during analysis?
Ensuring data privacy and security involves implementing encryption, access controls, anonymizing sensitive data, and adhering to regulatory standards such as GDPR or HIPAA to protect data throughout the analysis process.
Does SLA provide EMI?
Yes, SLA provides EMI options with 0% interest.
Is data analysis a good career for freshers?
Yes. A profession as a data analyst is highly recommended for 2024 and beyond. Data analysts assist businesses in making decisions in the modern environment, where they mainly rely on data.
What is the difference between Data Analytics and Data Science?
Data Analytics is the practice of extracting insights from data. Data Science is an interdisciplinary field that combines data engineering, computer science and analytics to solve complex issues. Data Science also uses algorithms to create predictions based on data.
Does data analytics need coding?
Although coding is not usually necessary for employment in data analysis, some data analysts do have to do it daily. Join our data analytics online training for a promising career.
What are some common challenges in data visualization?
Common challenges include choosing the right type of visualization for the data, ensuring clarity and readability, avoiding misleading representations, and effectively communicating insights to diverse audiences.
Are SLA’s classrooms modern?
Yes, all of SLA’s classrooms are smart classrooms, with modern technologies like computers and monitors to facilitate an efficient way of teaching.
What programming languages will I learn during this training?
During this training, you will learn to use R and Python for data analysis, machine learning, and artificial intelligence.





