With the rise of big data, data analytics skills are in high demand. Jobs requiring data analytics skills pay well and are growing rapidly. However, learning data analytics can seem daunting to beginners. In this article, we’ll look at what’s involved in learning data analytics and whether it’s as hard as it seems.
What is Data Analytics?
Data analytics refers to techniques for extracting insights from data. It involves using specialized tools and algorithms to clean, organize, analyze, visualize and extract meaning from raw data.
The goal is to discover patterns, trends and relationships within large volumes of data to support better business decision making. For example, a retail chain could use data analytics to analyze customer purchasing patterns and adjust marketing campaigns and inventory planning accordingly.
Why Learn Data Analytics?
Here are some of the key reasons why data analytics skills are valued in today’s job market:
- High demand – Data analyst is one of the hottest roles in IT according to research from LinkedIn and other job sites. Demand for data skills is spread across many industries.
- Well paid – Average salaries for data analysts range from $60,000 to over $100,000 per year according to Glassdoor salary data.
- Future proof – As data volumes grow, organizations require increasingly sophisticated analytics capabilities. Data analyst skills will remain relevant.
- Varied options – Data analysts can work in a wide range of sectors from finance and healthcare to marketing and logistics.
- Interesting work – Data analysts get to investigate data, spot trends and make an impact with their findings.
In summary, data analytics skills open up ample job opportunities, great salaries, career stability and interesting work. Let’s look at what’s involved in building these skills.
Key Skills Needed
While some analyst roles require a degree in statistics, computer science or analytics, it’s also possible to transition into analytics from other fields. The key skills you need to get started are:
- Math – Being comfortable with math, statistics and quantitative reasoning helps with analyzing numerical data.
- Technical – Learn a programming language like Python or R to automate data tasks.
- Data wrangling – Be able to process, clean and restructure datasets for analysis.
- Data analysis – Master fundamental analysis techniques like regression, clustering and decision trees.
- Data visualization – Present data insights through effective visualizations and dashboards.
- Business – Understand how to apply analytics to solve real-world business problems.
Beyond these core skills, communication and collaboration abilities are also important for sharing data insights effectively.
How Hard is it to Learn?
The main challenges faced by beginners are:
- Learning programming – Coding can be tough initially especially for those without a technical background.
- Statistics – Having a good grasp of statistical concepts takes some studying.
- Too many tools – With so many data tools and languages to choose from, deciding where to start can be overwhelming.
- Making it practical – Bridging theory and practical application takes time and experience.
However, while learning data analytics does require effort, it’s not impossible for beginners to break into the field. Here are some tips:
- Start simple – Focus first on core skills like basic coding, SQL, Excel and data visualization.
- Use online courses – Take advantage of high quality and affordable online classes to build skills.
- Do projects – Work on real or simulated datasets to practice analysis end-to-end.
- Get mentorship – Connect with experienced data professionals who can provide guidance.
- Build a portfolio – Create projects and samples to showcase skills you acquire.
With the right strategy, beginners can steadily gain data analytics skills even if they are starting from scratch.
How Long Does it Take?
The time taken to become job ready as a data analyst depends on your initial skill level and learning pace.
Here’s a rough timeline for building core data analytics skills:
Months | Learning Focus |
---|---|
1-3 months | SQL and Excel fundamentals |
3-6 months | Data processing and cleaning with Python / R |
6-9 months | In-depth data analysis methods |
9-12 months | Advanced analysis, visualization and storytelling |
With consistent effort, most learners can develop job ready skills within a year. Some with relevant backgrounds or who study full-time may be ready within 6 months.
Ultimately how long it takes depends on:
- Your prior experience – Having technical or quantitative skills accelerates learning.
- Study time – Learning part-time while working takes longer than full-time study.
- Learning style – Self-driven learners may progress faster than structured classroom styles.
- Goals – If upskilling for a specific role, you can tailor learning to required skills.
The key is setting milestones and giving yourself enough time to thoroughly acquire the foundational knowledge. Rushing the learning process without mastering basics can backfire in job interviews.
Tips for Learning Efficiently
Here are some tips to help you maximize your learning efficiency as a data analytics beginner:
- Take online courses – Structured online programs allow learning at your own pace.
- Learn by doing – Apply concepts to real datasets instead of just passive learning.
- Join forums – Discuss challenges and insights with peers along the way.
- Experiment with tools – Get hands-on with leading analytics tools like Python, R, SQL, Tableau, etc.
- Set milestones – Break learning into smaller milestones to stay motivated.
- Do personal projects – Work on analytics projects for your own interest and learning.
- Read case studies – Learn how analytics solves real business problems.
- Participate in hackathons – Apply skills under time pressure.
- Follow thought leaders – Stay inspired and up-to-date by following analytics experts.
The combination of structured learning and practical application is the fastest path to building in-demand data analytics skills.
Learning Resources
Here are some of the best resources for mastering data analytics skills:
Online Courses
- Coursera – Offers certificates and degrees in data science, including a popular introduction course and IBM data science program.
- edX – Data science programs from top institutions including MITx MicroMasters in Statistics and Data Science.
- Udacity – Nanodegree programs focused on data analytics and data science.
- Udemy – Affordable a la carte online data analytics, data science and Tableau courses.
- DataCamp – Specializes in data analytics and data science courses using R and Python.
Books
- Naked Statistics by Charles Wheelan – Engaging introductory statistics book for non-technical readers.
- Python for Data Analysis by Wes McKinney – Hands-on Python for data wrangling and analysis.
- Storytelling with Data by Cole Nussbaumer Knaflic – Visualization and communication for analysts.
- The Art of Statistics by David Spiegelhalter – Stories and principles of data analysis.
- R for Data Science by Hadley Wickham – Core data analysis skills using the R language.
Online Resources
- KDnuggets – Leading site for news and insights on data analytics and data science.
- Towards Data Science – Practical articles and tutorials for intermediate to advanced analysts.
- DataCamp Community – Active community forums monitored by data science experts.
- DataQuest – Blog with tutorials covering data skills for beginners.
- Reddit Data Science – Discussions on latest data analytics tools, techniques and topics.
YouTube Channels
- Khan Academy – Clear videos on statistics, algebra, calculus and more.
- Data School – Introduction to data science concepts and techniques.
- Siraj Raval – Data science and AI tutorials with Python code examples.
- StatQuest with Josh Starmer – Animated videos on statistical thinking.
- Data Viz Mastery – Techniques for taking data visualization skills to the next level.
These resources are a great starting point for building data skills at your own pace.
Conclusion
While learning data analytics presents some challenges, it is possible for motivated beginners to develop valued data skills within a year or less. The key is using high quality learning resources, getting practical hands-on experience, setting milestones to stay on track and seeking guidance when needed. With the right strategy, both technical and non-technical learners can transition into data analytics roles and thrive in this fast-growing field.