Data Analytics For Professionals
Atul HarshaSenior Manager Content
In the digital age, data is the cornerstone of decision-making. The surge in data analytics jobs and the promising data analytics career path have led many individuals to explore this burgeoning field. Enrolling in a data analytics online course is a step towards gaining expertise and understanding the nuances of this domain.
Understanding Data Analytics
Data analytics involves examining raw data to draw conclusions and insights. It's a subset of business analytics, focusing on processing and interpreting complex datasets to inform business decisions. Tools like NVivo and KNIME are often used for managing and analyzing data.
Latest Statistics and Data Analytics Trends for 2023
- The global market for data analytics is expected to be worth over $100 billion in 2023.
- The analytics market size is seen to grow to a $57 million industry by 2023.
- 91.9% of organizations achieved measurable value from data and analytics investments in 2023.
- More businesses will operationalize AI, and analytics will become more pervasive, democratized, and composable in 2023.
- The big data analytics market is set to reach $103 billion by 2023
- Investors have poured in $43.8 billion in 1,300 big data analytics companies as of February 2023
- Poor data quality costs the US economy up to $3.1 trillion
- The big data analytics market soared by 62%, from $169 billion in 2018 to $274 billion in 2022, and is anticipated to create $103 billion by 2027
How to become a Data Analyst?
Becoming a data analyst requires a combination of education, skills acquisition, and practical experience. Below is a step-by-step guide on how to become a data analyst:
| Category | Action Items | Details |
|---|---|---|
| Educational Background | A. Obtain a Bachelor’s Degree | - Opt for degrees in Statistics, Mathematics, Information Technology, or Business. |
| B. Take Relevant Courses | - Focus on courses in statistics, mathematics, computer science, and business. | |
| Technical Skill Development | A. Learn Programming Languages | - Become proficient in Python or R. |
| B. Master Database Management | - Learn SQL for effective database management. | |
| C. Learn Data Visualization Tools | - Familiarize with tools like Tableau or PowerBI. | |
| Knowledge in Data Analytics | A. Enroll in Online Courses and Certifications | - Choose platforms like Coursera, edX, or Udemy. |
| B. Understand Data Processing | - Learn about data cleaning, transformation, and processing. | |
| Practical Experience | A. Work on Real Projects | - Engage in real-world data analytics projects. |
| B. Create a Portfolio | - Showcase your data analytics projects and skills. | |
| Job Application Process | A. Prepare Resume and Cover Letter | - Highlight skills, education, and experience. |
| B. Apply for Entry-Level Positions or Internships | - Target relevant data analyst positions. | |
| Continuous Learning & Networking | A. Pursue Advanced Degrees or Certifications | - Consider further education in data analytics or related fields. |
| B. Join Data Analytics Forums and Groups | - Network with professionals and stay updated on industry trends. | |
| Career Advancement | A. Gain Experience in the Field | - Work in various roles to accumulate experience. |
| B. Continuously Update Skills | - Learn about the latest tools, technologies, and best practices in data analytics. |
This tabular guide provides a clear and organized pathway for aspiring data analysts. Following these steps, continuously learning, and adapting to new trends and technologies in the field will help individuals successfully establish a career in data analytics.
What Skillsets are required to become a Data Analyst in 2024?
| Skill | Description | Real-Life Application |
|---|---|---|
| 1. Statistical Knowledge | Understanding of statistical concepts and methodologies. | - Conducting hypothesis testing to validate assumptions and make informed decisions. - Utilizing probability distributions to model uncertain events in data analysis. |
| 2. Programming Skills | Proficiency in languages like Python or R for data manipulation and analysis. | - Using Python to automate the collection and cleaning of data from multiple sources. - Employing R for advanced statistical analysis and data visualization. |
| 3. SQL Expertise | Ability to write and execute complex SQL queries for database management. | - Writing SQL queries to join multiple tables from a database, providing a comprehensive dataset for analysis. - Utilizing SQL for data filtering and sorting to facilitate efficient analysis. |
| 4. Data Visualization | Capability to create clear, insightful visual representations of data. | - Creating interactive dashboards using Tableau to present data insights to stakeholders. - Designing clear and concise data visualizations to represent complex data trends and patterns. |
| 5. Data Cleaning | Skills to clean and organize raw data into a usable format. | - Employing data cleaning techniques to handle missing or inconsistent data, ensuring the reliability of the dataset for analysis. - Using data transformation methods to normalize and standardize data. |
| 6. Problem-Solving Ability | Capacity to approach and solve complex issues analytically. | - Identifying the root cause of data discrepancies and developing strategies to address them. - Analyzing large datasets to uncover hidden patterns and insights to solve business problems. |
| 7. Machine Learning Knowledge | Understanding of machine learning algorithms and their application. | - Implementing machine learning algorithms to develop predictive models for customer behavior. - Utilizing machine learning to automate the classification and segmentation of data. |
| 8. Business Acumen | Insight into business operations and strategies. | - Analyzing sales and customer data to provide actionable insights for marketing strategies. - Utilizing data analysis to optimize supply chain operations and improve business efficiency. |
Career Path of a Data Analyst
This is a career path for someone who wants to start their journey as a data analyst in 2024. It considers the evolving trends in data analytics, the increasing integration of AI and machine learning, and the continuous demand for advanced analytical skills.
| Year | Position | Responsibilities | Skills and Tools |
|---|---|---|---|
| 2024 | Entry-Level Data Analyst | - Collecting and interpreting data - Analyzing results - Reporting the results back to relevant teams | - Excel, Basic SQL, Basic Statistical Analysis, Data Visualization Tools (e.g., Tableau) |
| 2026 | Intermediate Data Analyst | - Mining data from primary and secondary sources - Cleaning and pruning data - Analyzing and visualizing data | - Advanced SQL, Python/R, Data Cleaning Tools (e.g., Trifacta), Advanced Data Visualization |
| 2029 | Senior Data Analyst | - Designing and maintaining data systems and databases - Troubleshooting data-related problems and authoring reports | - Machine Learning Algorithms, Advanced Analytics Tools (e.g., SAS, Apache Hadoop), ETL Tools (e.g., Talend) |
| 2032 | Data Scientist | - Creating advanced analytics models - Utilizing machine learning and AI to analyze data | - Advanced Machine Learning, AI Integration, Big Data Tools (e.g., Spark, Hive), Cloud Platforms (e.g., AWS, Azure) |
| 2035 | Data Analytics Manager | - Overseeing analytics teams - Developing strategies for data analysis and reporting | - Leadership Skills, Project Management Tools (e.g., Jira), Data Governance, Strategic Planning |
| 2039 | Director of Data Analytics | - Directing and organizing the analytics department - Ensuring data accuracy and usability | - Strategic Thinking, Organizational Skills, Change Management, Advanced Data Strategy |
| 2044 | Chief Data Officer (CDO) | - Overseeing the organization's data and analytics strategy - Ensuring data governance and compliance | - Executive Leadership, Policy Knowledge, Risk Management, Corporate Strategy |
Note:
- The above table outlines a potential career progression for a data analyst starting in 2024. The years are indicative and represent the time spent in each role, which may vary for different individuals.
- Continuous learning and upskilling are crucial at every step to stay abreast of the latest trends and technologies in the data analytics domain.
- Additional certifications and qualifications in relevant areas will further enhance career progression opportunities.
Who can Enroll? Data Analytics Online Course Eligibility Criteria
| Who Can Enroll? | Background | Objective |
|---|---|---|
| 1. Students | - Students from various fields including Statistics, Mathematics, Computer Science, Information Technology, and Business. | - To gain foundational and advanced knowledge in data analytics. - Enhance future career prospects and prepare for data analytics roles. |
| 2. Working Professionals | - Professionals from sectors like IT, Finance, Marketing, and Healthcare. | - To upskill and transition into data analytics roles. - Leverage data analytics for better decision-making in their current roles. |
| 3. Entrepreneurs/Business Owners | - Individuals who own or manage a business. | - To understand and utilize data analytics for improving business decision-making and strategies. |
| 4. Career Changers | - Individuals from diverse professional backgrounds seeking a career shift. | - To acquire the necessary skills and knowledge for a successful career in data analytics. |
| 5. Freelancers | - Independent professionals offering various services. | - To add data analytics to their skill set and provide additional services to clients. |
Data Analytics Online Course
Online data analytics courses are ideal for working professionals who wish to learn data analytics along with their day-to-day job.
| Course Name | Skills You'll Gain | Rating | Level | Duration |
|---|---|---|---|---|
| Google Data Analytics | Data Analysis, Data Management, Business Analysis, SQL, Spreadsheet Software, Data Analysis Software, Data Visualization, Databases, Critical Thinking, Data Structures, Problem Solving, Programming Principles, and more. | 4.8 (127.7k reviews) | Beginner | 3 - 6 Months |
| IBM Data Analyst | Data Analysis, Python Programming, Data Visualization, Computer Programming, Data Management, Statistical Programming, Exploratory Data Analysis, Data Analysis Software, Statistical Analysis, Data Structures, Basic Descriptive Statistics, and more. | 4.6 (69.3k reviews) | Beginner | 3 - 6 Months |
| Introduction to Data Analytics | Data Analysis, Data Management, Big Data, Basic Descriptive Statistics, Data Visualization, Databases, Python Programming, Cloud Computing, Data Mining, Machine Learning, Data Science, Microsoft Excel. | 4.8 (12.8k reviews) | Beginner | 1 - 3 Months |
| Google Advanced Data Analytics | Communication, Computer Programming, Data Analysis, Exploratory Data Analysis, General Statistics, Machine Learning, Probability Distribution, Project Management, Python Programming, Regression, Statistical Analysis, Tableau Software. | 4.7 (1.7k reviews) | Advanced | 3 - 6 Months |
| Business Analytics with Excel: Elementary to Advanced | Business Analysis, Data Analysis, Data Visualization, Spreadsheet Software, Data Model, Decision Making, Microsoft Excel, Process Analysis, Statistical Visualization. | 4.7 (739 reviews) | Intermediate | 1 - 3 Months |
Data Analytics Syllabus
A data analytics course consists of the following topics:
| Syllabus of Data Analytics | ||
|---|---|---|
| Data Structures and Algorithms |
Probability and Statistics |
Relational Database Management Systems |
| Business Fundamentals |
Text Analytics |
Data Collection |
| Data Visualization |
Statistical Analysis |
Forecasting Analytics |
| Simulation |
Machine Learning |
Optimization |
| Marketing Analytics |
Pricing Analytics |
Social Network Analysis |
| Retail Analytics |
Customer Analytics |
Supply Chain Analytics |
Some of the software tools taught in the course are:
| R |
Power BI |
| MySQL |
MongoDB |
| Hadoop |
Tableau |
| Python |
Excel |
| SAS |
Scala |
Check out data analytics interview questions to ace the interview round
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Top Companies Hiring Data Analytics Professionals
In today’s world, Data Analytics is the need of the hour. If a business enterprise has to excel in its field, it has to take the help of data analysis and data science. With the help of data analytics companies can not only increase their revenue, expand the customer base, provide better customer satisfaction, cut cost, but also get an edge over their competitors.
Data Analysts are the most sought-after skilled professionals in the current world, not only in the Information Technology industry but also in Finance, Insurance, Banking, Marketing and sales, Consumer Goods, Electronics, Heavy Industries and all other sectors; as all of them require someone to do the number crunching for them.
| Companies Hiring Data Analytics Graduates | ||
|---|---|---|
| |
MachinePulse |
The Smart Cube |
| Fractal |
Amazon |
Novartis |
| Fractal Analytics |
JP Morgan Chase |
Capital One |
| RBS |
ICICI Bank |
Flipkart |
| Kotak Mahindra |
Accenture Consulting |
KPMG |
| Yahoo |
|
Tumblr |
| Tredence |
Cartesian Consulting |
Quantium Analytics |
| Deloitte |
HSBC |
Credit Suisse |
| Barclays |
Axis Bank |
E-bay |
| Expedia |
AT Kearney |
CRISIL |
| Microsoft |
|
Mu Sigma |
| BRIDGEi2i |
Flytxt |
Tata iQ |
| Citi Bank |
Deutsche Bank |
Nomura |
| HDFC Bank |
Yes Bank |
Snapdeal |
| Make My Trip |
E&Y |
Walmart Labs |
| |
|
ZS Associates |
| Brillio |
Bank of America |
- |
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Student Forum
Anything you would want to ask experts?Answered a week ago
Chandigarh University ensures strong placement support for BE Data Science students as Big Data and Analytics comes under that program. The programme includes IBM mentorship, lab-based learning, and industry-focused projects to develop practical expertise. Students learn advanced topics like AI, mac
A
Beginner-Level 4
Answered a week ago
Chandigarh University offers strong placements for CSE students specializing in Big Data and Analytics. This specialisation is part of the BE CSE–Data Science programme and is AICTE-approved. The programme is industry-sponsored with IBM collaboration, which ensures that students gain practical skill
M
Beginner-Level 3
Answered 2 weeks ago
CSE placements at Chandigarh University, in general, are robust and structured, providing students with excellent career opportunities. The university has a dedicated training and placement cell that prepares students through coding workshops, mock interviews, and aptitude tests. Companies from top
Beginner-Level 5
Answered 2 weeks ago
From my perspective, BE in CSE with a specialisation in Big Data and Analytics at Chandigarh University is highly beneficial for career growth. The placement opportunities are solid, with companies from IT services and product sectors recruiting students skilled in analytics. Engineering dept also e
P
Beginner-Level 1
Answered 2 weeks ago
It is a smart choice to take admission in Chandigarh University for CSE with IBM Data and Analytics. This four year programme is made for the rising need of data experts in IT. The course helps students to understand data Science concepts and use them for solving business problems.
Here, students lea
P
Beginner-Level 4
Answered 2 weeks ago
At Chandigarh University, the focus is on making B.E. CSE Big Data and Analytics students fully prepared for placements. The faculty in the Engineering Department guide us through real-world projects and hands-on labs, teaching tools like SQL, Hadoop, and Tableau. CU also organizes internships, work
N
Beginner-Level 5
Answered 3 weeks ago
The Binghamton University provides and MS in Data Analytics program which offers students with advanced skills in statistical analysis and data-driven decision-making. The programme electives focus on technical and analytical areas such as machine learning, predictive analytics, data mining, and inf
S
Contributor-Level 10
Answered a month ago
I completed Uncodemy data analytics course in Noida a year ago, and it turned out to be a great investment. The teaching was clear, hands-on, and closely aligned with industry needs. The mentors were highly supportive, and the placement team actively guided me toward landing my first role in analyti
M
Beginner-Level 1
Answered 2 months ago
Masters in Data Analytics has become a very popular course for international students in the UK. The reasons why UK is good for this course are given as follows:
- Academic Excellence: United Kingdom is known to have many globally renowned universities for Masters in Data Analytics with good quality of
Contributor-Level 10
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How is the placement in BE CSE- Big Data & Analytics in Chandigarh University?