Whether you become a Data Analyst by choice, or by chance, in both the cases you are expected to possess some essential skills. Let us understand the skills most companies demand by looking at the Job Description of Data Analyst profile.
Data Analyst Job Description
The ideal Data Analyst candidate should possess strong skills in data mining, generation, and visualization. They are expected to develop and maintain advanced analytics systems, create best-practice reports, and identify trends and growth opportunities through complex data analysis. The role involves evaluating organizational methods, ensuring data processing accuracy, and collaborating with managers to gather requirements and deliver actionable insights. Proficiency in SQL or Excel and technical writing experience in queries, reports, and presentations are essential. Additionally, the candidate should create and maintain interactive visualizations, develop scalable data acquisition processes, and address inefficiencies in internal systems to drive innovation and growth.
The requirements defined above in the job description are comprehensive and demanding, reflecting the critical nature of the position in driving business growth and innovation. From a candidate’s perspective, meeting these requirements means being strong at data skills, possessing effective communication abilities, and a proactive approach to problem-solving and innovation. The role is challenging but offers lots of opportunities for those who are skilled and motivated to make a substantial impact on any organization’s success.
Becoming a Data Analyst
Once you start your journey to become a Data Analyst, you should focus on learning key skills. If you are from a non-technical background, you should start by understanding the end-to-end data analysis process covering data preparation, analysis of raw data, developing visualisations and generating insights. For this, you need to start by learning MS Excel, database skills (SQL), statistics, and become proficient in popular data analytics tools like Power BI, Tableau and sometimes, even Python.
The next step is to look for your first Data Analyst job, but if you struggle to find a job, look for internship opportunities for a couple of months. For this, you should start sharing your skills in your online profiles, prepare a professional Data Analyst resume and keep applying for jobs, directly or through references. In short, the whole process until you grab a job will take a few months, and patience is the key. If your budget allows, go for Data Analytics certifications offered by various platforms like Google, Coursera, Tableau, Microsoft, etc.
Once you land your first job as a Data Analyst, the door of opportunities opens for you, your focus should be on learning as much as possible on hands-on projects. The more you learn in the initial years of your career, the more clarity you will have about your future career path.
Career Path for a Data Analyst
As a data analyst, your focus should be to acquire as much experience as possible with a variety of data, this will help you prepare well for different challenges and opportunities in your career and make you an expert data analyst. To understand the various career paths available for data analysts, you need to understand the end-to-end process of Data Science.
The above process can be broken down into sub-processes and there are different roles defined for each of them.
- Data Engineer for automating the process of data collection
- Data Scientist for building machine learning models
- Business Intelligence (BI) Analyst for Visualisation and Reporting
- Data Analyst for Data Preparation, Data Analysis and Visualisation
All these roles require different technical skills and experience with different sets of tools, with experience as a Data Analyst and the kind of work you do on a daily basis, you can gain more clarity on which role you want to be in next.
1. Growing as a Data analyst
As the case is with any other career, the first option is to grow as a data analyst following the path where you grow from Data Analyst to Senior Data Analyst to Data Analytics Manager and then move into a leadership role. It is important to gain experience of 1-2 years in a data analyst role before you start thinking about the next level, the more experience you gain as an analyst the more chance you have of climbing up the ladder as a senior analyst or a manager.
How fast you progress depends upon the size and growth prospects of the organisation you work for, and the industry you work for. The more quickly you start taking ownership of the data-related work in your organisation, the more the probability of you getting noticed for the next role.
2. Growing as a Data Engineer
Analysing the available data is one part of the Data Science process, fetching the data from the source and preparing it in a particular format to make it ready for further analysis is another part where your role as a Data Engineer is to use tools to create a pipeline of data which takes data from different sources, transform as needed, and then uploads the integrated data to the destination (either in form of a file or on to a destination database).
Being a Data Engineer requires you to be more technical as compared to being a Data Analyst. You need to understand the whole process of ETL (Extract, Transform and Load), and the tools needed to automate the process of ETL and should be able to architect a complete ETL solution from a variety of data sources to a variety of target destinations.
3. Growing as a Data Scientist
The next part of the Data Science process is to Build Machine Learning (ML) models. This is the most challenging as well as interesting part. To become a Data Scientist, you should be well-versed in Statistics, Probability, and Mathematics. Some people would differ here as they would feel that being good at Mathematics is not a requirement as most of the maths is done by the tools, however, that is what differentiates good data scientists from the mediocre ones. A good understanding of Algebra, Differentiation, Integration, Vectors and Calculus can help you become a great Data Scientist as you will be able to create your own algorithms and solve some real complex problems.
One important task that a Data Scientist should be constantly doing is searching for the latest research and scientific papers published by researchers in various journals. Data Science is an evolving field and people are continuously building upon the research work done by others.
If you want to become a sought-after Data Scientist, you should have a good understanding of Supervised and Unsupervised learning, Regression and Classification, Natural Language Processing (NLP), Neural Networks and should be able to create models, and evaluate their performance in Python. Python is not difficult as long as you are aware of which libraries to use and when.
All the Large Language Models (LLMs) including OpenAI’s GPT have been created in Python and most of the Data Scientists use Python, or R for their projects. Jupyter Notebook is an essential tool for Data Scientists where they can easily write pieces of code with detailed comments and explanations about the code.
To summarise, you need to learn different ML algorithms and Python/R to become a Data Scientist.
4. Growing as a Business Intelligence (BI) Analyst
A BI Analyst is very similar to a Data Analyst, but here, your core competency is building effective Visualisations using different tools like Power BI, Tableau, Qlik, etc. A few years back, all the reporting to senior management used to happen in MS Excel, but it has some limitations, hence, as the volume and velocity of data have increased, most organisations have shifted their reporting to these BI tools which gives them the capability to easily generate and share insights to take decisions based on data.
As a BI Analyst, you are expected to be creative in designing professional dashboards. You should know which type of chart should be used to show a particular insight. You should be able to tell stories using the dashboards. You should be able to connect to different sources of data to prepare your visualisations which should have the capability to update the dashboards as and when the data is refreshed.
5. Growing as a Domain Expert with Data Skills
One of the most important but not easily available skills is the knowledge of domain along with Data Analytics skills. Organisations operating in specific industries require Data Analysts to be able to make business sense of the data. Most of the time, Data Analysts just work as technical experts who know how to generate insights from data, but understanding specific domains is an added value that every organisation would love to have. For example, having a good understanding of data generated in the Telecom industry can help you grow quickly in the organisation as a Data Analyst, similarly, if you understand the kind of data generated in the eCommerce industry, Finance industry, Retail industry, Airlines industry, it will be much easier for you to become a domain expert with Data skills and be in demand for it.
Conclusion
Before you get attracted by the growing demand for Data Analytics professionals in the market, assess your interest and analytical ability to check if you are suitable for the role or not. You should be willing to solve business problems with data, or sometimes, find the problems that data can solve. You should first gain clarity on the end-to-end data analytics or data science process before deciding on your career path. It is ok to be an expert in one part of the process while you have a high-level understanding of the remaining process, however, developing skillsets to cover other parts of the process will only take you in the direction of becoming an expert in Data Analytics, which is what most of the organisations desire to have, and are even ready to pay lucrative salary packages for. Having domain knowledge along with data skills is a killer combination and organisations in specific industries would compete to hire you if you can make business sense of their data.
To help you on this journey, Techcanvass offers a range of specialized courses like
- Data Analytics Certification Training
- IIBA Data Analytics Training(CBDA)
- Data Analytics Fundamentals Course
- Data Visualization Training in Tableau
- Data Visualization Training with Power BI
These courses are tailored to provide you with comprehensive training, ensuring you are well-prepared to meet industry demands and excel in your data analytics career.
