A Detailed Guide to Data Analyst Course Syllabus

May 27, 2023 Deepak Nagar 0 Comments

Data analysis is essential to making critical strategic decisions in today’s data-driven environment. Data analysis is therefore essential to the survival and development of every organization. In addition to helping organizations make thoughtful, well-informed decisions, data analysis has seen an increase in interest in India. Expert data analysts are in high demand today. A wide variety of courses are available to help you understand and study the detailed material in the data analyst course syllabus.

This article will guide you through how to choose the best data analyst course syllabus

Definition of Data Analytics

Using specialist hardware and software to analyze data collection is known as data analytics (DA). Data analytics is typically used to identify trends and make judgments about the facts contained in them. Data analytics tools and methodologies are widely used in the commercial sector to help companies make better business decisions.

Scientists and researchers use analytical tools to support and refute scientific hypotheses, models, and ideas. There are several ways in which data analytics strategies can be used to support firms in their efforts to improve customer service, enhance marketing campaigns, and generate revenue.

Companies can also use data analytics to react swiftly to changing market trends and gain an advantage over their competitors. In data analytics, the primary objective is to improve business performance, but depending on the specific application, evaluated data could include new data gathered for real-time analytics or archived documents.

Additionally, it may be derived from both internal and external sources. A variety of frameworks and tools are used during the analytical process to visualize the research findings. The type of data collected can be structured, semi-structured, or unstructured.

Data analysts are among the most exciting career options among the many employment types in the data analytics industry. They can assist businesses in transforming raw data into useful knowledge for corporate success. Getting a better understanding of what a data analyst is and discussing the data analyst course syllabus is the next step!

Definition of Data Analyst

Businesses generate and collect data no matter what size they are. Data analysts are the ones who gather, prepare and analyze large datasets. Client reviews, financial records, logistics, market research, etc., are examples of this information.

The domains of data analysts are data management, data modeling, and reporting. In addition to enhancing the customer experience, pricing new goods, and reducing transportation costs, a data analyst uses this data to determine various actions.

As soon as you learn who a data analyst is, you must understand their functions and responsibilities. Data analysts work in the fields of reporting, data modeling, and data management.

Data Analyst Course Syllabus

As part of the data analyst course syllabus, specialized software or systems are used to teach comprehensive methods for extracting, analyzing, and manipulating data. Studying topics such as Mathematics and Statistics, Data Structures, Stimulation, Collecting data, and comparable ones are intended to prepare the same individuals.

While a data analyst course syllabus may vary from program to program, some of the most used topics of data analytics are as under:

Data Structures and Algorithms

Data structures and algorithms are crucial topics in the data analyst course syllabus because they are essentially named places where data may be organized and stored. Data structures and algorithms are essentially a series of steps used to solve a specific problem.

The following are some of the concerns related to structures and algorithms:

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

Probability and Statistics

The whole point of probability and statistics is to deal with the relative frequency of events. Probability is concerned with estimating how similar future events will be, whereas statistics look at frequency.

The main branches of probability and statistics are as follows:

Business 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, Central Tendency Measures, and Dispersion Measures
Graphical Techniques, Skewness & Kurtosis, Box Plot
Descriptive Stats
Sampling Funnel
Sampling Variation
Central Limit Theorem
Confidence interval

Business Fundamentals

These are the core skills required to comprehend the numerous components of corporate management.

The main topics covered by business basics are:

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

This automated process converts huge amounts of unstructured text into quantitative data to spot trends, insights, and patterns.

The following are the main topics in text analytics:

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

Also Read: Data Analytics Scope

Data Collection

It is a technique for assessing and accumulating information about certain variables in an already-existing system, enabling the user to compute results.

The main topics under study 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 be aware of the skills necessary to become successful data analysts. Additionally, one needs to have strong numerical and analytical skills as well as a deep understanding of computer tools like Microsoft Succeed, Python, SQL, R, and the Statistical Language to excel at data analytics (R).

Skills That Are Included in the Data Analyst Course Syllabus Are:

Excel: Basics to Advanced

It is used to create grids with text, numbers, and formulas to manipulate data and track expenses, financial results, and other things.

The topics discussed in the Microsoft Excel course syllabus are as per the following list:

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 and Clearing Formatting
Using Conditional Formatting to Hide Cells
Using the IF Function
The “Value if false” Condition is changed to Text
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 to its Sheet
Adding Data Labels
Exploding a Slice of a Pie Chart
Data Analysis − Overview

Types of Data Analysis

Data Analysis Process
Working with a range of 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 Basics

Python can be thought of as sophisticated object-oriented programming. It is the software that lends itself to developing the best because of its flexible binding, high-level built-in structures, and language. Support is provided for packages and modules that provide the modularity and programmability of programs.

The following is a list of the topics covered in the curriculum for the Python course:

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 statements
Comments
Python Data Structures & Data Types
String Operations in Python
Simple Input & Output
Simple Output Formatting
Deep copy
Shallow copy
Operators in python

You can find here the best Data Analytics Books

R Programming

The R programming language enables statistical approaches and data visualization. It is used by specialists, data analysts, and other experts because it is made up of a collection of libraries.

The subjects covered in the data analyst course syllabus for the R programming course are listed below:

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

SQL

A language known as SQL, or Structured Query Language, can be used to communicate with databases. Data extraction, manipulation, and management are made easier with the help of the Relational Database Management System (RDMS). Many various database systems, including Oracle, MySQL, and others, support this programming language.

The subjects covered by the course syllabus for the SQL course are listed below:

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
Table Management and Creation 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

You can prepare, analyze, collaborate on, and integrate your big data results with the help of Tableau, a comprehensive data analytics platform. With Tableau, users can swiftly communicate their findings across the organization and pose fresh inquiries concerning controlled big data. Tableau is a Master of Visual analysis for self-service.

The subjects covered by the Tableau course syllabus are listed below:

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
Reporting Totals, Subtotals, and Grand Totals

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
Chart with Side by Side Bars or Grouped Bars
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

You should consider these highly recommended courses to become data analysts

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
Map box 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 the Dashboard
Dashboard Objects
Create a Story

Module 7: Server

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

Power BI

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 constantly updating 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
Put text, a map, and gauge visualizations together to make a report
Use a slicer to filter visualizations
Sort, copy, and paste visualizations
Use a custom image you downloaded from the gallery

Module 3: Reports and Dashboards

Modify and Print a Report
Rename and delete report pages
A page or report should have a filter
Set visualization interactions
Print a report page
Send a report to PowerPoint
Create a Dashboard
Create and manage dashboards
Pin a report tile to a dashboard
Pin a dashboard’s live report page
Pin a tile from another dashboard
Pin an Excel element 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 to share a tile
Embed a report in SharePoint Online

Module 5: Other Power BI Components and Table Relationship

Use Power BI Mobile Apps
Get Power BI for mobile
In the iPad app, you may view reports and dashboards
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
Get Power BI Desktop data with the Power BI service
Export a report from the Power BI service to the Desktop

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

Machine learning is an application of artificial intelligence that allows computers to automatically learn and develop without having been explicitly created. It highlights the development of computer programs that can get data and use it for independent research. To find patterns in the data and enhance decisions, this course begins with data observations, such as first-hand experience or advice.

The topics covered in the syllabus for the machine learning course are listed below:

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

The Data Analyst Course Syllabus Software and Tools

Data analytics are becoming more and more necessary, and as a result, numerous solutions with different functionalities have been developed. The top data analytics tools, whether they are open-source or user-friendly, are listed below.

Tableau

Any data source, including Excel, Corporate Data Warehouse, and others, is freely accessible using this program, which subsequently creates maps, visualizations, and web-based interfaces with real-time updates.

QlikView

It offers quick in-memory data processing, with the end user receiving the results. Along with data association, data visualization, and data compression are all included.

Python

An open-source object-oriented programming language is easy to comprehend, develop, and maintain. TensorFlow, Matplotlib, Scikit-learn, Pandas, and Keras are just a few of the machine learning and visualization modules it offers. This tool can be developed on any platform, including a SQL server, MongoDB database, or JSON.

RapidMiner

Any type of data source, such as Tera data, Oracle, or SQL Server, can be used with this tool because it is an effective integrated space.

Frequently Asked Questions About Data Analyst Course Syllabus

Q1. What is Data Analytics?

Data analytics is the process of gathering, purifying, and analyzing unstructured data to find significant trends, patterns, and insights.
Data insights can be utilized to uncover solutions to even the most difficult situations and answers to a wide range of inquiries.
Data analytics enables us to comprehend the impacts of our current actions, potential future actions, and any likely results of a different course of action.
Data analytics essentially tells us what to do next. The goal is to use objective data to support well-informed judgments that aid a project in achieving its objectives.

Q2. How difficult is it to learn Data Analytics?

The job of a data analyst is not as intellectual as the title might imply, believe it or not. You don’t have to be an expert programmer or mathematician to succeed in this field. Being a successful data analyst requires a wide range of abilities, some of which are highly technical. Many of the most complex abilities used by a data analyst can be picked up on the job, but it’s crucial, to begin with, a strong foundation of the fundamental tools and methods. Anyone interested in entering the industry should think about obtaining a formal certification as the likelihood of learning things alone online is remote.

Q3. Would you recommend a career in data analytics?

Since there is a high demand for their skills and a small pool of skilled individuals, data analysts make good career alternatives. Additionally offered are generous benefits packages and high salary rates.

Q4: I don’t have any prior experience. Can I still work as a data analyst?

Without any past professional experience, it is feasible to become a data analyst. The best part of being a data analyst is getting to do that. The data analyst course syllabus covers education levels from beginner to advanced.

Q5: What is the difference between data analysis and data analytics?

Analytics differ from analysis in that they are scalable. Data analysis falls under the generic phrase of “data analytics.” Data analysis is the process of looking at data. Data analysis includes data gathering, organization, storage, analytical methods, and tools.

Conclusion

We have finally completed the guide on the Data Analyst course syllabus. You were given an explanation of data analytics and the topics included in the Data Analyst course syllabus. Data analytics is one of the industries with the highest demand and the possibility for the most lucrative career advancement. From the role of Data Analyst, you can go up to Senior Analyst, Data Scientist, Chief Technology Officer, and Analyst II.

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