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.
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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 are some frequent data analytics topics:
| Data Structures and Algorithms | Pricing Analytics |
| Supply Chain Analytics | Data Collection |
| Probability and Statistics | Marketing Analytics, |
| Customer Analytics | Data Visualization |
| Relational Database Management Systems | Optimization |
| Retail Analytics | Statistical Analysis |
| Business Fundamentals | Machine Learning |
| Social Network Analytics | Forecasting Analytics |
| Text Analytics | Simulation |
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Data Analytics Engineering Syllabus
The curriculum of engineering Big Data Analytics involves the following topics:
| Engineering Mathematics | Principles of Programming | Data Structures |
| Operating Systems | Computer System Architecture | Python Programming |
| Object Oriented Programming | Design and Analysis of Algorithm | Software Engineering & Project management |
| Big Data Ingestion | Design Thinking | Formal Languages and Automata Theory |
| Probability & Statistics for Engineers | Advanced Database Management Systems | Data Communication and Computer Networks |
| Computer Graphics | Big Data Storage | Disk Based Processing |
| Modelling and Simulation | Human Computer Interface | Compiler Design |
| In Memory Processing | Advanced Functional Thinking | Cryptography and Network Security |
| Image Processing & Pattern Analysis | Stream Processing | Big Data Search |
| Software Reliability & Testing | Software Quality Assurance | Data Exploration at Scale |
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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 | Loops |
| Syntax | Python Lists |
| Functions, Function Arguments, and Control Flow | Modules |
| Decorators and Generators | 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 |
Interview Questions for You to Prepare for Jobs
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.
Data Analytics Course Syllabus PDF
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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.
