In today’s data-driven environment, businesses and organizations rely on data analysis to make decisive strategic choices crucial to the development and survival of every firm. Organizations make well-informed and thoughtful decisions with the aid of data analysis. Since expert data analysts are in high demand right now, many course options help study and grasp the comprehensive material in the data analyst course syllabus.
What is Data Analytics?
Data analytics is a multidisciplinary field that employs various analysis techniques, including math, statistics, and computer science, to draw insights from data sets. It is a broad term that includes everything from simply analyzing data to theorizing ways of collecting data and creating the frameworks needed to store it.
Once you know what data analyst is, to get to the bottom of the data analyst course syllabus, let’s move on to the following part.
Data Analyst Course Syllabus
With various software or specialized systems, the data analyst course syllabus is a comprehensive instruction in data extraction, analysis, and manipulation. It intends to educate the same through topics like Mathematics and Statistics, Data Structures, Stimulation, Data Collection, and Comparison.
Although the data analyst course syllabus varies from curriculum to curriculum or program to program, the following is our structured data analytics syllabus:
| Topics | Sub-topics |
| Overview of Excel | Introduction to Data Analytics Basic Features in Excel Formatting in Excel Dealing with Raw Data Functions in Excel |
| Deep Dive with Excel – II | Data Connectors in Excel Cleaning in Power Query Editor Adding Conditional Columns using Power Query Editor Data Modelling and its Importance Cardinality and Filter Direction in Power Pivot |
| Master Advanced Excel – III | Pivot Tables in Excel Charts in Excel Slicers in Excel Measures in Excel Creating a Dashboard in Excel |
| MySQL | Introduction to MySQL Basic MySQL Syntax Clauses in MySQL Operators in MySQL Dealing With Null Values in MySQL |
| Advanced SQL Queries and Functions in MySQL | Functions in MySQL Case Operator in MySQL Group By in MySQL Having Clause in MySQL Joins in MySQL |
| Advanced SQL Concepts and Techniques in MySQL | Subqueries in MySQL Union, Intersect, Except in MySQL Stored Procedures in MySQL Common Table Expressions(CTE) Window Functions in MySQL |
| Data Visualisation with Power BI | Introduction to Power BI Data Connectors Power Query Editor and Tools Power Query Editor Append Queries and Merge Queries Pivoting and Unpivoting of data |
| Advanced-Data Modeling and DAX in Power BI | Data Modelling and Cardinality Cross Filter Direction Measures vs Calculated Columns Functions in DAX Cumulative Sales and Moving Average Using DAX |
| Visualizations and Dashboard Creation in Power BI | ChatGPT for Measures Column Charts and Slicers Matrix vs Tables Cards, KPI and Gauge Chart Formatting a Dashboard |
| Data Visualisation with Tableau | Overview of Tableau Software Connecting to Data Sources (Excel, CSV, Database) Charts in Tableau Sorting, Grouping, and Filtering Data Using Colors, Labels, and Tooltips |
| Advanced Data Visualization and Storytelling with Tableau | Creating Geographic Maps Adding Actions to Dashboards Create Data Hierarchies Using Tableau Stories to Create a Narrative Sharing Dashboards and Visualizations |
| Maths & Applied Statistics | Introduction to Statistics Descriptive Statistics Hypothesis Testing AB Testing Fundamentals of Probability |
| Python Basics | Introduction to Python for Data Analytics Datatypes and Variables Operators in Python Control Flow in Python Data Structures in Python |
| Python Advance | Functions in Python In-built Modules Pickle Library Introduction to Numpy Statistical Functions in Arrays |
| Python Libraries – I | Introduction to Pandas Cleaning Data with Pandas Merge, concatenate and join – Pandas Introduction to Matplotlib Charts in Matplotlib |
| Python Libraries – II | What is WebScraping BeautifulSoup and Requests Library in Python Extracting Data from Tables Extracting Data from Multi-Page Websites Text Analysis using Python |
| Machine Learning – I | What is ML? Applications of Machine Learning Linear Regression Decision Trees Random Forests and Ensemble Methods |
| Machine Learning – II | Clustering Algorithms Hierarchical clustering Dimensionality Reduction Cross-Validation Techniques Evaluation metrics and ROC curves |
| AI for Data Analytics | Generating code & debugging errors Exchanging Data Cleaning Skills Crafting SQL queries Optimizing Model Selection with AI-driven Methods Utilizing AI for Anomaly Detection in Data Analysis |
If you wish to enhance your Data Analytics skills, WsCube Tech’s Data Analytics courses will surely help you. To explore Data Analytics courses, check the Online Data Analytics Course, which consists of 20 weeks of online live classes.
Data Analytics Skills
To be good at data analytics, candidates must have strong numerical and analytical skills with a proper understanding of computer software like Python, SQL, Statistical Language (R), Machine Learning, and Microsoft Excel.
1. Python
| File Operations using Python | Python Loops |
| Python Syntax | Python Lists |
| Python Functions, Function Arguments, and Control Flow | Python Modules |
| Python Decorators and Generators | Python Dictionaries |
| Errors and Exception handling | Comparisons and Operators |
2. Microsoft Excel
| Create Workbooks | Data Formatting |
| Formulas Usage | Slicers |
| Create Pivot Tables | Graphs |
| Cell Referencing | Functions and Formulas |
| Charts Edit | VBA |
3. R Programming
| Background and Nuts & Bolts | Loop Functions and Debugging |
| Programming | Simulation & Profiling |
4. SQL
| Basic Concepts | Database Creation |
| Entity-relationship Modeling | Adding records to Table |
| Relational Model | SQL Subqueries |
| Data Manipulation | SQL Injections |
5. Machine Learning
| Introduction to Learning Methods: Supervised, Unsupervised, and Reinforcement Learning | Data Preprocessing and Data Mining |
| Decision Tree | Database & SQL |
| Linear Regression | Exploratory Data Analysis |
| SNM | Logistic Regression |
| CNN | Naive Bayes |
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Data Analytics Books
| Books | Authors |
| The Elements of Data Mining, Statistical Learning, Inference, and Prediction | Robert Tibshirani, Trevor Hastie, Jerome Friedman |
| Data Analysis Software: Programming with R (Statistics and Computing) | Robert Tibshirani, Trevor Hastie, Jerome Friedman |
| Probability & Statistics for Engineers & Scientists | Ronald E. Walpole, Sharon L. Myers, Raymond H. Myers, and Keying Ye |
| Data Mining and Analysis | Mohammed J. Zaki, Wagner Meira |
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Data Analytics Tools
Many tools with several functionalities are available with the growing demand for Data Analytics in the market. Whether it is user-friendly or open-source, the following are some of the top tools in data analytics.
| Tool | Details | Organizations Using |
| R and Python | R and Python, the programming languages are used in the Data Analytics field. R tool is used for Statistics and Analytics, whereas Python is a high-level interpreted language. | ANZ, Google, Firefox, YouTube, Netflix, Facebook |
| Microsoft Excel | Microsoft Excel provides users with features like sharing workbooks, working in real-time collaboration, adding data to Excel directly from a photo, and many more features. | McDonald’s, IKEA, and Marriott |
| Tableau | Tableau allows you to work on a live data set and spend less time on Data Wrangling. It is a free tool. | Citibank, Deloitte, Skype, Audi |
| RapidMiner | RapidMiner, a tool for data processing and building Machine Learning models. Studio, GO, Server, Real-Time Scoring, and Radoop are the products of RapidMiner. | BMW, Hewlett Packard Enterprise, EZCater, Sanofi |
| KNIME | Konstanz Information Miner (KNIME) is free and open-source for data analytics, reporting, and integration. KNIME provides two software: the KNIME Analytics Platform and the KNIME Server. | Siemens, Novartis, Deutsche Telekom, Continental |
| Apache Spark | Apache Spark provides an open-source community and an interface for programming that identifies any fault tolerance and implicit data parallelism. The current version of Apache Spark is version 2.4.5. | Oracle, Hortonworks, Verizon, Visa |
| Power Bl | Power BI is a Microsoft-based product used in business analytics. Power BI Embedded, Power BI Premium, Power BI Desktop, Power BI Mobile, Power BI Pro, and Power BI Report Server are the products of Power BI. | Adobe, Heathrow, Worldsmart, GE Healthcare |
| QlikView | QlikView is a Business Intelligence, Data Visualization, and Data Analytics tool that helps accelerate business value. Various tools and services are available for free or a free trial of 30 days. | CISCO, NHS, KitchenAid, and Samsung |
| Talend | Talend is a tool that efficiently manages all the steps involved in the ETL process and delivers accessible and clean data for users. Stitch Data Loader, Talend Pipeline Designer, Talend Open Source, Talend Cloud Data Integration, and Talend Data Fabric are free or available for 14 days. | ALDO, ABInBev, EuroNext, AstraZeneca |
| Splunk | Splunk is a tool to analyze the data gathered from applications and websites. Splunk Free, Splunk Enterprise, and Splunk Cloud are the features of Splunk that can be accessed for free for a limited period. | Dominos, Otto Group, Intel, Lenovo |
Entrance Exams for Data Analytics
A few of such entrance exams are discussed as:
- JEE MAINS & JEE Advanced (To pursue bachelors in data science)
- CUET (Conducted by Christ University)
- CAT
- MAT
- GATE
- JNUEE (Conducted by Jawaharlal Nehru University)
- NIMSEE (Conducted by NIMS University)
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Distance Education in Data Analytics
An online distance program for Data Analytics is usually a full-time course. The duration of most of the distance courses is 12 months. Most courses are recognized and accepted for employment in private and state sectors. Several universities offer data analytics online courses via distance education.
Candidates unable to afford full-time courses can get their degrees through distance education. Also, students who cannot meet the eligibility requirements for full-time courses opt for part-time or distance data analytics programs. The syllabus of distance education programs is remarkably similar to regular programs.
Data Analytics Course Eligibility
To become a Data Analyst, a student must have an undergraduate or postgraduate degree in Data Analytics or related fields like Data Science, Big Data, Artificial Intelligence, Machine Learning, Computer Science, Mathematics, Statistics, etc. Students must check the eligibility criteria for different Data Analytics courses before admission.
| Course | Eligibility |
| Diploma or Postgraduate Diploma in Data Analytics | The student must have graduated in Science or Engineering with Maths/Statistics/CS/IT as one of the core subjects and secure more than 50% marks in the qualifying exam. |
| Undergraduate Data Analytics Courses | The student should pass classes 10 & 12 with more than 50 – 60% marks in the qualifying exam with basic knowledge of Statistics, Mathematics, and Programming. |
| Postgraduate Data Analytics Courses | The student must have graduated in Science or Engineering with basic to advanced knowledge of Statistics, Mathematics, and Programming and secure more than 50 – 70% marks in the qualifying exam. In some cases, work experience is also required. |
Apart from this, given below professionals are also eligible to pursue courses in Data Analytics:
- Non-IT Professionals
- BI Professionals
- Data Analytics Professionals
- Project Managers
- Software Developers
- Information Architects
Data Analytics Course Fees
Data Analytics course fees depend on the course type you are pursuing. Data Analytics Certification course is a lot more affordable compared to a PostGraduate Degree in Data Analytics:
- Data Analytics Certification course fees level is ₹ 5000 – 10,000
- Data Analytics Diploma course fees s ₹ 9,300 – 2,00,000
- Data Analytics course fees at the Undergraduate level are ₹ 90,000 – 21,00,000
- Data Analytics course fees at the Postgraduate level are ₹ 20,000 – 23,00,000
Also, the Data Analysis course fee depends on the course duration, the distinctive features each institute provides, and the post-course assistance. Online and offline institutes provide high-grade coaching, so sometimes, the syllabus isn’t the determinant of the difference in data analytics course fees for various institutes. Practical projects and access to learning portals, study materials, etc., can be significant factors for the difference in data analytics course fees.
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Career Scope in Data Analytics
Jobs in Big Data Analytics are high-paying. Data is endless, and so will the jobs in data analytics. After the whole world has gone digital, data has become the most important entity for all businesses. Not just in the private sector, but government sectors also depend greatly on data.
Sectors where Big Data Analytics is majorly implemented:
- Banking
- Education
- Government
- Healthcare
- Marketing
- E-commerce
- Entertainment
- Research and development
Job Roles in Data Analytics
| Data Analyst | Big Data Visualizer | Big Data Engineer |
| Data Strategist | Big Data Specialist | System Administrators |
| Network Managers | Database Administrator | Big Data Architect |
Top Recruiters in Data Analytics
| Amazon | Flipkart | |
| Reliance | Accenture | |
| TCS | Wipro | Citrix |
| IBM | Deloitte | |
| Ernst & Young | L&T | HCL |
Data Analytics Course Benefits
Data analyst courses provide candidates with the needful skills and knowledge to interpret and analyze data effectively. Here are some benefits of taking a data analyst course:
1. Learn essential data analysis tools and techniques
Data analyst courses teach students essential tools and techniques like SQL, R, Excel, and Python. These tools are widely used in the industry and are crucial for data analysts to analyze data effectively. Students can improve their analytical skills and become more valuable to potential employers by learning these tools and techniques.
2. Develop critical thinking and problem-solving skills
Data analysis involves a lot of critical thinking and problem-solving. Students who take data analyst courses learn how to approach problems analytically and develop solutions based on data-driven insights. These skills can be applied in several fields, again making data analysts highly sought after by employers.
3. Gain industry-relevant knowledge
Data analyst courses are designed to provide students with industry-relevant knowledge. This includes understanding how data analysis is used in different industries and how to apply data analysis techniques to solve real-world problems. This knowledge is precious and can help students stand out from other job candidates.
Conclusion
In conclusion, the data analytics course syllabus encompasses a broad range of skills and modules, nurturing individuals into adept data detectives capable of deciphering the language of numbers and making impactful business decisions. This comprehensive syllabus doesn’t just teach skills; it empowers individuals to shape the world with data-driven insights.
Regardless of whether you prefer an online course or a class or a full-time university course, the Data Analytics syllabus and curriculum tend to be almost the same worldwide. In each course, projects can vary. However, for every Data Analytics course syllabus, the basic principles of Data Science and the basics of Data Analytics are compulsory.
FAQs on Data Analytics Course Syllabus
The data analyst course syllabus includes various topics like Data collection, visualization techniques, statistics, probability algorithms, and algorithms, and much more are covered in the course.
Data analysts are excellent career choices. There’s a strong demand for their services and a small pool of qualified candidates. High-pay packages and fantastic benefits are also provided.
Both course syllabuses share commonalities like statistics, data manipulation, and data visualization. However, the data scientist course syllabus goes deeper into machine learning algorithms and deep learning and often includes more complex topics like artificial intelligence.
The data analyst job is not as intellectual as the title implies. You don’t have to be an expert mathematician or programmer to succeed in this field. Being a successful data analyst requires several abilities, a few of which are highly technical. Most of the challenging abilities data analysts use can be picked up on the job, but it’s essential to begin with a robust foundation of the fundamental tools and methods.
Without any prior professional experience, it is feasible to become a data analyst. The foremost part of being a data analyst is getting to do that. The data analyst course syllabus covers everything from beginner to advanced.
To become a data analyst, acquire a strong foundation in statistics and programming, and learn relevant tools like Excel, Python, SQL, and data visualization software. Also, gain hands-on experience through projects and internships to showcase your analytical skills.
The key topics covered in data analytics are:
-Introduction to Data Analytics
-Basic Features in Excel
-Formatting in Excel
-Data Connectors in Excel
-Cleaning in Power Query Editor
-Adding Conditional Columns using Power Query Editor
-Data Modelling and its Importance
-Statistical Analysis
-SQL
-Exploratory Data Analysis (EDA)
-Data Visualization
–Python libraries
-Big data tools
-Artificial intelligence and Machine learning
-Hands-on projects and case studies
Most data analytics syllabus covers multiple tools and technologies, such as Python (NumPy and Pandas) and R for data manipulation; Hadoop and Spark for big data; Tableau, Seaborn, Power BI, and Metplotlib for data visualization; and SQL and NoSQL for databases.
A typical data analytics course spans 6-8 months. A short-term course will span for 3-5 months, whereas a full-time course is for 9-12 months. You can also join bootcamps that span 6-10 weeks.
Most data analytics courses cover R, Python, and SQL as the key programming languages. The three are easy to learn and suitable for beginners.
We have designed an extensive data analytics syllabus that also includes advanced tools and libraries like Power BI, Tableau, and Python-based tools.
Yes, our data analytics syllabus includes multiple hands-on projects that include critical concepts like fraud detection, sales forecasting, market basket analysis, and sentiment analysis.
Data analytics is not difficult to understand but it demands dedication, consistent practice, and real-time implementation of skills and knowledge through hands-on experience.
