A Detailed Guide To Data Analyst Course Syllabus – Fee, Duration And More

Businesses and organizations rely on data analysis to make critical strategic choices in today’s data-driven environment. Therefore, data analysis is crucial to the survival and development of every firm. Organizations may make thoughtful, well-informed decisions with the aid of data analysis. India also sees a rise in interest in data analysis. Expert data analysts are in high demand right now. Many different course options may help you study and grasp the detailed material in the data analyst course syllabus.

What is Data Analytics?

Analyzing data collections to identify trends and make judgments about the facts they contain is known as data analytics (DA). More often than not, specialist hardware and software are used to do data analytics. To help businesses, make better business decisions, data analytics tools and methodologies are widely employed in the commercial sector. Analytics tools are also used by scientists and researchers to support or refute scientific models, ideas, and hypotheses.

Data analytics strategies may support firms’ attempts to boost customer service, enhance marketing campaigns, and generate revenue. Analytics also provides companies the ability to react swiftly to changing market trends and acquire an advantage over competing companies. The fundamental goal of data analytics is to enhance business performance, though. Depending upon the specific application, the data that is evaluated may be made up of new data that has been collected for real-time analytics or archival documents. Additionally, it could originate from a combination of both internal systems and outside data sources.

The type of data gathered may be organized, semi-structured, or unstructured. Visual representations of the end results as graphs and charts that show the research’ precise findings are available. A variety of frameworks and tools are used during the analytical process.

Professionals that can assist businesses in transforming raw data into knowledge that is useful for corporate success are in great demand. Data analysts have some of the most exciting career options among the many employment types in the data analytics industry. The next step is to comprehend what a data analyst is. Also, discuss the data analyst course syllabus!

Data Analysts: who are they?

The collection, preparation, and review of large datasets are dared by data analysts. Every business, regardless of size, generates and collects data. This information may take the shape of client reviews, financial records, logistics, market research, and so on.

A data analyst is a specialist who uses this data to determine various actions, including, to name a few, how to enhance customer experience, price new goods, and lower transportation costs. Data management, data modeling, and reporting are the domains of data analysts.

It is crucial to understand a data analyst’s functions and responsibilities after learning who they are.

Once you are aware of who is a data analyst. To comprehend the data analyst course syllabus, let’s move on to the following part.

Data Analyst Course Syllabus

With the assistance of various software or specialized systems, the data analyst course syllabus is created to offer comprehensive instruction in data extraction, analysis, and manipulation. Through the study of topics like Mathematics and Statistics, Data Structures, Stimulation, Collection Of data, and comparable ones, it intends to educate the same.

Although the data analyst course syllabus may vary from program to program or curriculum to curriculum, the following are some frequent data analytics topics:

Data Structures and Algorithms

The difference between a data structure, which is essentially a named place that may be used to organize and store data, and an algorithm, which is a series of steps used to solve a certain issue. Data structures and algorithms are a very crucial topic in data analyst courses syllabus.  Data structure and algorithmic concerns include:

• Array, Iteration, and Invariants
• List, recursion, stacks, and queues 
• Efficiency and complexities
• Trees
• Hash Tables
• Binary search trees
• Searching
• Sorting

Probability and Statistics

This is the entire purpose of probability and statistics to handle the relative occurrence of events. While statistics examine the frequency, the probability is concerned with predicting how similar future events will be. These are the primary areas of probability and statistics:

Introduction to Statistical Analysis

• Counting, Probability, and Probability Distributions
• Sampling Distributions
• Estimation and Hypothesis Testing
• Scatter Diagram
• ANOVA and Chisquare
• Imputation Techniques
• Data Cleaning
• Correlation and Regression

Introduction to Data Analytics

• Data Analytics Overview
• Importance of Data Analytics
• Types of Data Analytics
• Descriptive Analytics
• Diagnostic Analytics
• Predictive Analytics
• Prescriptive Analytics
• Benefits of Data Analytics
• Data Visualization for Decision Making
• Data Types, Measure Of central tendency
• Measures of Dispersion
• Graphical Techniques
• Skewness & Kurtosis
• Box Plot
• Descriptive Stats
• Sampling Funnel
• Sampling Variation
• Central Limit Theorem
• Confidence interval

Business Fundamentals

These are the fundamental abilities needed to comprehend the many parts of managing a corporation. Business basics’ primary subjects include:

• Teamwork in Business
• The foundations of Business
• Ethics and Social responsibility
• Structuring organizations
• Motivating Employees
• Managing Human resources
• Economics of Business
• Operations Management

Text Analytics

To identify trends, insights, and patterns, this automated procedure transforms massive amounts of unstructured text into quantitative data. The primary subjects in text analytics are:

• Natural language basics
• Processing and understanding text
• Text Summarization
• Text similarity and Clustering 
• Text classification
• Semantic and Sentiment analysis

Data Collection

It is a method of gauging and gathering data on certain variables in an already-existing system, allowing the user to compute outcomes. The primary subject areas for data collection are:

• Survey Sampling
• Observational result
• Statistical Techniques
• Analysis of Unstructured Data
• Extracting and Presenting Statistics

Data Visualization

Data visualization’s principal subjects include:

• Java
• CSS
• Customized geographic map
• Creation of Bar Chart
• Scatter Plot

Skills Included in Data Analyst Course Syllabus

Candidates need to understand the abilities that will enable them to land a successful job as data analysts. Additionally, to excel at data analytics, one must possess strong numerical and analytical abilities as well as a thorough grasp of computer programs like Microsoft Excel, Python, SQL, R, and the Statistical Language (R).

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Skills that are included in the Data Analyst Course Syllabus are:

Microsoft Excel Basic to Advanced

It is utilized to build text, number, and formula grids that aid in data manipulation to track expenditures, financial outcomes, and other things.

The following list includes the topics discussed in the Microsoft Excel course syllabus:

• Excel tutorial
• Text to Columns
• Concatenate
• The Concatenate Function
• The Right Function with Concatenation
• Absolute Cell References
• Data Validation
• Time and Date Calculations
• Conditional Formatting
• Exploring Styles
• Clearing Formatting
• Using Conditional Formatting to Hide Cells
• Using the IF Function
• Pivot Tables
• Creating a Pivot Table
• Specifying PivotTable Data
• Changing a PivotTables Calculation
• Filtering and Sorting a PivotTable
• Creating a PivotChart
• Grouping Items
• Updating a PivotTable
• Formatting a PivotTable
• Using Slicers
• Charts
• Creating a Simple Chart
• Charting Non-Adjacent Cells
• Creating a Chart Using the Chart Wizard
• Modifying Charts
• Moving an Embedded Chart
• Sizing an Embedded Chart
• Changing the Chart Type
• Chart Types
• Changing the Way Data is Displayed
• Moving the Legend
• Formatting Charts
• Adding Chart Items
• Formatting All Text
• Formatting and Aligning Numbers
• Formatting the Plot Area
• Formatting Data Markers
• Pie Charts
• Creating a Pie Chart
• Moving the Pie Chart
• Adding Data Labels
• Exploding a Slice of a Pie Chart
• Data Analysis − Overview
• types of Data Analysis
• Data Analysis Process
• Working with Range Names
• Copying Name using Formula Autocomplete
• Range Name Syntax Rules
• Creating Range Names
• Creating Names for Constants
• Managing Names
• Scope of a Name
• Editing Names
• Applying Names
• Using Names in a Formula
• Viewing Names in a Workbook
• Copying Formulas with Names
• Difference between Tables and Ranges
• Create Table
• Table Name
• Managing Names in a Table
• Table Headers replacing Column Letters
• Propagation of a Formula in a Table
• Resize Table
• Remove Duplicates
• Convert to Range
• Table Style Options
• Table Styles
• Cleaning Data with Text Functions
• Removing Unwanted Characters from Text
• Extracting Data Values from Text
• Formatting Data with Text Functions
• Date Formats
• Conditional Formatting
• Sorting
• Filtering
• Lookup Functions
• Pivoting

Python

One may categorize Python as advanced object-oriented programming. With its flexible binding, high-level built-in structures, and language, it is the program that lends itself to developing the best. Packages and modules that guarantee program modularity and programmability are supported.

The Subjects Covered by the Python Course Curriculum Are Listed Below:

• File operations using Python
• Looping in Python
• Python Syntax
• Functions
• Function Arguments, and Control Flow
• Working with Lists
• Python Modules
• Decorators and generators
• Using Dictionaries
• Errors and Exception Handling
• Comparisons and Operators
• The print statement
• Comments
• Python Data Structures & Data Types
• String Operations in Python
• Simple Input & Output
• Simple Output Formatting
• Deep copy
• Shallow copy
• Operators in python

R Programming

Data visualization and statistical methods are made possible with the R programming language. Specialists, data analysts, and other professionals utilize it because it comprises a compilation of libraries.

The Following List Includes the Subjects Taught in the R Programming Course Syllabus:

• Background and Nuts & Bolts
• Programming
• Loop Functions and Debugging
• Simulation and Profiling

SQL

Databases may be contacted using a language called SQL, or Structured Query Language. Utilizing the Relational Database Management System, aids in the extraction, manipulation, and management of data (RDMS). This programming language is supported by several different database systems, including Oracle, MySQL, and others.

The Following List Includes the Subjects Taught in the SQL Course Syllabus:

• Introduction to Oracle Database
• Retrieve Data using the SQL SELECT Statement
• Learn to Restrict and Sort Data
• Usage of Single-Row Functions to Customize Output
• Invoke Conversion Functions and Conditional Expressions
• Aggregate Data Using the Group Functions
• Display Data from Multiple Tables Using Joins
• Use Sub-Queries to Solve Queries
• The SET Operators
• Data Manipulation Statements
• Creating and Managing Tables Using DDL Statements
• Other Schema Objects
• Control User Access
• Management of Schema Objects
• Manage Objects with Data Dictionary Views
• Manipulate Large Data Sets
• Data Management in Different Time Zones
• Retrieve Data Using Sub-queries
• Regular Expression Support

Tableau

A complete data analytics tool called Tableau enables you to prepare, analyze, work together, and integrate your big data findings. With Tableau, users can quickly share their findings throughout the enterprise and ask new questions about managed large data. Tableau excels at self-service visual analysis.

The Following List Includes the Subjects Taught in the Tableau Course Syllabus:

Module 1: Tableau Course Material

• Start Page
• Show Me
• Connecting to Excel Files
• Connecting to Text Files
• Connect to Microsoft SQL Server
• Connecting to Microsoft Analysis Services
• Creating and Removing Hierarchies
• Bins
• Joining Tables
• Data Blending

Module 2: Learn Tableau Basic Reports

• Parameters
• Grouping Example 1
• Grouping Example 2
• Edit Groups
• Set
• Combined Sets
• Creating a First Report
• Data Labels
• Create Folders
• Sorting Data
• Add Totals
• Sub Totals
• Grand Totals to Report

Module 3: Learn Tableau Charts

• Area Chart
• Bar Chart
• Box Plot
• Bubble Chart
• Bump Chart
• Bullet Graph
• Circle Views
• Dual Combination Chart
• Dual Lines Chart
• Funnel Chart
• Traditional Funnel Charts
• Gantt Chart
• Grouped Bar
• Heatmap
• Highlight Table
• Histogram
• Cumulative Histogram
• Line Chart
• Lollipop Chart
• Pareto Chart
• Pie Chart
• Scatter Plot
• Stacked Bar Chart
• Text Label
• Tree Map
• Word Cloud
• Waterfall Chart

Module 4: Learn Tableau Advanced Reports

• Dual Axis Reports
• Blended Axis
• Individual Axis
• Add Reference Lines
• Reference Bands
• Reference Distributions
• Basic Maps
• Symbol Map
• Use Google Maps
• Mapbox Maps as a Background Map
• WMS Server Map as a Background Map

Module 5: Learn Tableau Calculations & Filters

• Calculated Fields
• Basic Approach to Calculate Rank
• Advanced Approach to Calculate Ra
• Calculating Running Total
• Filters Introduction
• Quick Filters
• Filters on Dimensions
• Conditional Filters
• Top and Bottom Filters
• Filters on Measures
• Context Filters
• Slicing Filters
• Data Source Filters
• Extract Filters

Module 6: Learn Tableau Dashboards

• Create a Dashboard
• Format Dashboard Layout
• Create a Device Preview of a Dashboard
• Create Filters on Dashboard
• Dashboard Objects
• Create a Story

Module 7: Server

• Tableau online.
• Overview of Tableau Server.
• Publishing Tableau objects and scheduling/subscription.

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Power BI

Data extraction and visualization are both made possible by the cloud-based analysis tool Power BI. To provide you with a complete picture of the information assets in your firm, Power BI combines data from several sources.

The Following List Includes the Subjects Taught in the Power Bi Course Syllabus:

Module 1: Introduction to Power BI

• Get Started with Power BI
• Overview: Power BI concepts
• Sign up for Power BI
• Overview: Power BI data sources
• Connect to a SaaS solution
• Upload a local CSV file
• Connect to Excel data
• Connect to a sample
• Create a Report with Visualizations
• Explore the Power BI portal

Module 2: Viz and Tiles

• Overview: Visualizations
• Using visualizations
• Create a new report
• Create and arrange visualizations
• Format a visualization
• Create chart visualizations
• Create a report using text, a map, and gauge visualizations.
• Use a slicer to filter visualizations
• Sort, copy, and paste visualizations
• From the collection, save and utilize a unique image.

Module 3: Reports and Dashboards

• Modify and Print a Report
• Rename and delete report pages
• Set visualization interactions
• Print a report page
• Send a report to PowerPoint
• Create a Dashboard
• Create and manage dashboards
• Attach a report tile to a dashboard.
• Pinning a dashboard’s live report page
• An additional dashboard tile is pinned.
• Attach an Excel component to a dashboard.
• Manage pinned elements in Excel
• Add a tile to a dashboard
• Build a dashboard with Quick Insights
• Set a Featured (default) dashboard
• Ask Questions about Your Data
• Ask a question with Power BI Q&A
• Tweak your dataset for Q&A
• Enable Cortana for Power BI

Module 4: Publishing Workbooks and Workspace

• Share Data with Colleagues and Others
• Publish a report to the web
• Manage published reports
• Share a dashboard
• Create an app workspace and add users
• Use an app workspace
• Publish an app
• Create a QR code
• Embed a report in SharePoint Online

Module 5: Related Tables and Some other Power BI Components

• Use Power BI Mobile Apps
• Get Power BI for mobile
• Use workspaces in the mobile app
• Sharing from Power BI Mobile
• Use Power BI Desktop
• Install and launch Power BI Desktop
• Get data
• Reduce data
• Transform data
• Relate tables
• Utilize the Power BI service to obtain Power BI Desktop data.
• Publish a report to your desktop using the Power BI service.

Module 6: DAX functions

• New Dax functions
• Date and time functions
• Time intelligence functions
• Filter functions
• Information functions
• Logical functions
• Math & trig functions
• Parent and child functions
• Text functions

Machine Learning

An application of artificial intelligence called machine learning enables computers to learn and improve automatically without even being explicitly designed. It draws attention to the advancements in computer programs that can obtain data and utilize it to conduct independent research. This course starts with data observations, such as first-hand experience or guidance to look for patterns in the data and improve decisions.

The Following List Includes the Subjects Covered in the Syllabus for the Machine Learning Course:

• Introduction to different Learning methods 
• Decision Tree
• Database and SQL
• Data Preprocessing and Data Mining
• Linear Regression
• Exploratory Data Analysis
• SVM
• Logistic Regression
• CNN
• Naive Bayes

Software and Tools Used in Data Analyst Course Syllabus

Due to the growing market need for data analytics, many solutions have emerged with various functions for this goal. The following are a few of the best data analytics tools, whether they are open-source or user-friendly.

Tableau

This program allows for free access to any data source, including Excel, Corporate Data Warehouse, and others, and then generates maps, visualizations, and interfaces with real-time updates for the web.

QlikView

It provides rapid in-memory data processing with results supplied to the end customer. It includes data compression together with data association and data visualization.

Python

It is simple to understand, create, and maintain an open-source object-oriented programming language. It provides several machine learning and visualization modules, including TensorFlow, Matplotlib, Scikit-learn, Pandas, and Keras. Any platform, such as a SQL server, MongoDB database, or JSON, may be used to create this tool.

RapidMiner

This tool is a potent integrated space that can be used with any form of data source, including Tera data, Oracle, Sybase, Microsoft SQL, Excel, Access, and others. The majority of its applications are predictive analytics, including text analytics, data mining, and machine learning.

OpenRefine

This program, commonly referred to as GoogleRefine, is a data cleaning tool that can assist you in cleaning up data for analysis. When parsing and transforming website data, it is employed to clean up messy data.

SAS

This technology, which can quickly be accessed and can analyze data from many sources, is a language of programming and framework for manipulation of data and analytics.

FAQs 

Q1. What is the Data Analyst course syllabus?

The data analyst course syllabus includes a broad variety of topics that students must understand. Data collection, visualization techniques, statistics and probability algorithms and algorithms, and many other topics are covered in the course.

Q2. What does data analytics entail?

Graduates in data analytics are sought in almost every sector. Students who want to make a significant career change and earn a lot of money may choose a career in data analytics. Aspirants that have a solid grasp of the industry may also research alternative career paths.

Q3. Is a career in data analytics desirable?

Data analysts are excellent career choices since there is a strong demand for their services and a small pool of qualified candidates. High pay packages and fantastic benefits are also provided.

Q4. Can I work as a data analyst without any prior experience?

Yes, it is possible to become a data analyst without having prior work experience. That is the finest aspect of working as a data analyst. Beginner to advanced levels of education is covered in the data analyst course syllabus.

Conclusion on Data Analyst Course Syllabus 

Finally, we have reached the end of the Data Analyst course syllabus. Data analytics and the concepts covered in the Data Analyst course syllabus were explained to you.

One of the professions with the highest demand and the potential for the most profitable career advancement is data analytics. You can advance from the position of Data Analyst to that of Analyst II, Senior Analyst, Data Scientist, or even Chief Technology Officer.