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How to become a Data Analyst with no Experience

how to become a data scientist with no experience


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Who is a Data Analyst?

Nowadays, companies receive a tremendous amount of information every day that can be used to optimize their strategies. To get insights from the massive data collected, they need a highly qualified professional: the Data Analyst.

The task of a Data Analyst is to process the varied data concerning the customers, the products or the performances of the company, in order to release indicators useful for the decision-makers. Thus, the information provided by the data analyst enables companies to define the products to be offered to customers according to their needs, the marketing strategy to adopt, or the improvements to be made to the production process.

Data Analyst Qualifications

If you have graduated from a data analysis program and have a high GPA, it should be easy for you to land an entry-level data analysis job. Even if you don’t have a specialization in data analysis, but have a degree in mathematics, statistics, or economics from a well-reputed university, you can easily land a data analysis entry-level job.

Most entry-level data analyst jobs require at least a bachelor level degree. Higher level data analyst jobs usually guarantee a higher pay and may require you to have a master’s or a doctoral degree. Having a master’s degree in Data Science or Business Analytics is very helpful. If you are interested in data analytics, you should consider earning a master’s degree.

Skills Required for a Data Analyst

Data analysts scrutinize information using various data analysis tools. The results that the data analysts derive from the data available are used by their employers or clients to make informed decisions.  

A successful data analyst possesses the following skills:

  • A high level of mathematical ability: Knowledge of statistics and a good comfort level with formulae is needed for analyzing data to provide real-world value. As a data analyst, you should have a good grasp of mathematics and you should be able to solve common business problems, for example, calculating compound interest, depreciation, statistical measures (for example, mean, median, mode) etc. Also, you should know how to use tables, charts, graphs, etc. to make the visualization of data more appealing. It is important to be comfortable with college-level algebra. Knowing linear algebra and multivariate calculus is very helpful for data analysts as they are all extensively used in performing Data Analysis.
  • Programming languages: As a data analyst, you should be proficient in at least one programming language. However, the more languages you are proficient in, the better it is. Popular programming languages that can be used to manipulate data are R, Python, C++, Java, MATLAB, PHP, etc.
  • Data Management and Manipulation: As a data analyst, you should be familiar with languages, such as R, HIVE, SQL, etc. Building queries to extract the desired data is an extremely important aspect of data analysis. Once you have analyzed the data, you would have to create accurate reports. Some common tools for doing the same are SAS, Oracle Visual Analyzer, Microsoft Power BI, Cognos, Tableau, etc.
  • Domain Knowledge and great Communication skills: A data analyst’s job is to provide detailed and accurate information to the decision makers. Hence, it is important for a data analyst to understand the specific user requirements along with having a deep understanding of the data. Good communication skills are important for collaboration with the various clients, executives, IT specialists, etc., so as to ensure that the data aligns well with the business objectives. Ultimately, the analysis done by a data analyst will be used to modify/improve some business process.
  • Microsoft Excel: Organizing data and calculating numbers are among the main tasks of data analysts, hence it is very helpful if you are comfortable with using Excel. There are many great online sources where you can learn how to use Excel to its full potential.

Data Analyst Career Path

Skilled data analysts are in demand in almost every sector. Hence, it doesn’t really come as a surprise that the predicted growth rate in demand for data analysts for the next seven years is 19%. In fact, data analysis is considered to be the most important skill so every professional should learn Data Science as soon as possible to excel in career.

Some industries where the demand for data analysts is quite high are as follows:

  • Market Research: 72% of marketers consider data analysis to be vital for thriving in the present marketing landscape. The success of the marketing campaigns can be understood using data analysis. Also, data analysis can be used by companies for market research before launching a new product or service.
  • Finance and Investments: Financial institutions generally have a requirement for entry-level data analysts as well as expert ones. At many financial institutions, such as investment banks, the most common career path taken by data analysts is that of management. If you prove to be the best among your peer group, you will be considered for promotion by the senior management as they will consider you as someone who could manage new hires well.
  • Sales: There is a lot of data related to sales of products and services in a company that is analyzed, which helps in increasing sales and customer satisfaction and also in identifying the potential sales barriers. Hence, a requirement for data analysts arises in this sector as well.

A data analyst fresher makes handsome salary and range of the salary depends on his/her expertise and skill-set. The skills required as a fresher may vary across the industry.

For example, the typical job of a Data Analyst is to run queries against the available data for finding the important trends and processing the data that might be of use to Data Scientists. In general, the Data Analysts are very good at database query languages, for example, SQL. They may also write scripts and produce visuals on the data available to them for better understanding.

A Data Scientist, on the other hand, builds models using Machine Learning. These models are used to make several predictions and can also explain the future of the organization. Data Scientists work closely with Data Analysts while preparing the data to be used for the machine learning models. However, the salaries of Data Scientists are much higher than that of Data Analysts because of very high demand and low supply.

Many Data Analysts gain the relevant skills and become Data Scientists. The transition to becoming a Data Scientist is not very difficult for Data Analysts since they already have some relevant skills. Many Data Analysts go on to become Data Scientists.  

The designations of a Data Analyst would depend on the company he/she works in. However, generally, the technical work of the Data Analysts keeps on decreasing and the managerial work keeps on increasing as they climb up the corporate ladder. After a certain point, the promotion starts to depend on the leadership and managerial skills. Hence, it is important for Data Analysts to work on their soft skills as well.

How to become a Data Analyst with no Experience?

To become a data analyst, you must first earn a Bachelor’s degree. This is a requirement for most of the entry-level data analyst positions. The relevant disciplines include Finance, Economics, Mathematics, Statistics, Computer Science, and Information Management.

Considering that you don’t have any prior work experience as a data analyst, the most important task is to gain relevant work experience. As with a majority of professions, work experience is invaluable for a data analyst too. Fortunately, because of a huge demand for data analysts, there are many data analysis internship opportunities. You can work as an intern, which would help you gain the relevant work experience and also add a star to your resume.

Data analysis deals with understanding changing trends and technologies, which makes it important for a data analyst to commit himself/herself to lifelong learning. You can take up MOOCs to ensure that you keep learning new things relevant to data analysis, which will help you stay ahead of the curve.

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2 Comments, RSS

  1. Avatar

    Sajid Abbas March 7, 2019 @ 1:47 pm

    Whole information counts a lot for me.

    Thank you so much.

    Sajid Abbas

  2. Avatar

    DAVID March 16, 2019 @ 11:34 pm