Data Science is one of the top-rated career paths and a professional journey that one can take to a successful and financially-secure future. From the latest reports, the global data analytics market is valued at $49 billion and is projected to grow at a rate of 30% year on year. This, therefore, triggers many questions including what is Data Science and what courses to study for one to become a successful Data Scientist.
Today we shall look at how to become a data analyst with no experience or without a formal data science degree. The reason we do this is that the raw data that an analyst will work with on a day-to-day basis is quite messy. This means that there is a major gap for skilled analysts because most Data Science-related courses such as data analysis all lack the very key facet which requires one to be practical and hands-on.
The courses do not enable a student to use any raw data, how to clean it, and finally, process it. This creates a sizeable gap between the skills you need on the job and the abilities you have acquired in training. Truth be told, real-world data is messy, so you need to know how to overcome this obstacle to become an independent data professional.
Data science is a field of study that combines industry or domain expertise, programming skills, and knowledge of statistics and mathematics not only extract but display and provide meaningful insights from raw data.
A useful data analytics course must therefore be a top-rated course and trusted by businesses worldwide to be viable. Let’s delve into the 7 steps on how to become a data analyst.
Step 1: Learning the Fundamentals of Data Analytics
To enter a data analysis career, it is key that you start by developing some foundational yet critical knowledge in the field. By gaining a broader overview of data analytics you will be able to comfortably yet authoritatively decide whether this career is a good fit for you while gaining job-ready skills.
What are some of the fundamentals of data analysis?
You will cover some of the following:
Basic theory about data analytics
This covers basic definitions such as who is a data analyst, what their role is, and bringing value to their company or organization.
Python and R. Python
The main programming languages used in Data Analytics: Python is the defacto and main programming which has been listed as the top language for data science and by and large data analysis and machine learning due to its versatile tools and rich libraries.
NumPy is essentially Python’s fundamental package for scientific computing and the main tool for computing mathematical and statical operations.
The panda’s library just like NumPy is the most popular Python tool for data manipulation and analysis. And is used to manipulate all information types including text, numerical tables, and time-series data.
How do we exchange information today? By using text files, you will need to learn the fundamentals of data importation and saving while using Python, pandas
A data analyst will be working mostly with datasets and this is where it is most key to know how to convert a raw dataset from external files into a workable Python format and thereafter manipulate this data.
Data cleaning, Data preprocessing, and Data visualization
These are the essential steps that data must go through to be visualized and usable, therefore a data analyst will be required to have the skills to clean, preprocess and visualize this data successfully and meaningfully.
Step 2: Mastering Data Analytics Skills (Improving technical skills)
To boost and power your data analyst you are required to learn the core principles of data analysis and more importantly gain hands-on skills practice. Here you’ll work with a variety of data sources, project scenarios, and data analysis tools, including Excel, SQL, and Python, to gain practical experience with data manipulation and applying analytical techniques.
Step 3: Working on Projects that have real data
The best way to gain skills in data analysis is to learn how to find value in real data and work with it in real-world settings. You can explore online for free programs or courses that provide include hands-on projects using real data sets. Free public data sets are also available plenty online that you can use to design your own projects.
Step 4: Practicing and Developing Visualizations
*Working on programs like
Data visualization is the practice of translating gathered and analyzed data or information into a visual context, such as on colorful maps or graphs, to make data easier for the human brain to conceptualize and understand.
A data analyst will thus be required to be clear and concise on the purpose and goal, understand their data, define and scope the intended audience, and develop the visualization using the most appropriate way such as Pie, Line, Bar charts and scatter plots.
The result has to be interactive and coherent especially important when compiling a big data set into a visualization enabling users to easily process information.
The hierarchy of data also shows the various data points in a relevant way for decision-makers. Here, an analyst can sort from highest to lowest to emphasize the largest values or display them in categories that are more important to users and in a prominent way.
Therefore the even the order in which data is displayed, the colors utilized, and the size of various elements in the chart will quickly help the intended audience interpret the data faster and easily
Businesses depend on data to make important decisions, as such data analysts are required to use programming languages, business intelligence software like PowerBI, and tools that have various useful statistical and visualization libraries including as we have seen above Python, R, Excel, and Tableau, Bokeh, Plotly, or Infogram.
Step 5: Data Skill and Knowledge Demonstration (showcasing your work)
In data analysis, you will be required to demonstrate knowledge and skill in how to retrieve and gather large data volumes, organize them, and draw practical conclusions and visually useful insights businesses can use to make better decisions and reach conclusions. Communicating the data findings is one of the most important yet underrated skills that, if mastered well, can give you a major head start and differentiate you from your peers.
Step 6: Data Analyst Certification
A good Professional Data Analyst Certification does not require any prior programming or statistical skills and is supposed to be suitable for all types of learners with or without college degrees. An example is the ALX Data Analyst Nano Degree 3-month program. It’s all you need to get started and start a career. ALX coupled with basic computer literacy, high school math, a liking for working with numbers, a willingness to learn, and a desire to enrich your profile with valuable skills will propel you into a life-changing career path
Step 7: Applying to a relevant job
After gaining basic experience working with data and presenting your findings in meaningful ways, you will be ready to start applying for entry-level data analyst jobs.
It is important to list your skills and portfolio. Here you prepare your resume to sell your hard-earned data analysis skills, you can go for positions you don’t feel 100-per cent qualified for.
Even if you are still in school or just completed the course, request your university’s career services office about open internship opportunities. Internships will provide you will opportunities for gaining real-world experience that you can list on your resume and apply what you’re learning on the job.