Data analysts work with copious amount of data and try to see patterns in it.
Recognizing these patterns can solve major problems for businesses or avoid them from occurring. The work of data analysts contributes significantly to a business, as stakeholders rely on their work to make critical business decisions. To be able to produce an output of such magnitude, data analysts require an extensive set of skills.
Before data analysts can recognize patterns, they need to collect data and clean data. A Data Analysts role can be summarized as – collect, clean, and analyze. Data available with companies is rarely available in one place and is often collected from several applications and tools. The responsibility here lies with data analysts to collect relevant data and produce insightful information using statistical analysis, mathematical and algorithmic formulas.
Skills required to become a Data Analyst
You will need a gamut of skills including extensive knowledge of Microsoft Excel, programming (R and Python), SQL, data mapping, and the ability to see patterns.
1. Microsoft Excel — It comes with a suite of Macros and formulas to see numbers and patterns. VLOOKUP, HLOOKUP, Pivot table among others are frequently used by data analysts.
2. Programming – R and Python are two widely-used languages for data-related tasks. R is a statistical computing language and supports a graphical interface for data manipulation. Diplo will allow you to manipulate data using R, and ggplot2 and reshape2 which will allow you to visualize data. Python will be easier to learn. Similar to R, you will need to learn NumPy, pandas, matplotlib, scipy, sci-kit to perform data manipulation, visualization tasks.
- Statistics – This is an essential part of data analysis. Several statistical techniques are required to see if data is correct and not misaligned. There two parts to statistics – inferential and descriptive. Mean, median, mode, standard deviation, and variance form part of descriptive statistics which essentially describes data. Inferential statistics allows you to make inferences (or predictions) from the data.
- Machine learning – Machine learning is a complex area to master. It is a crucial area for data analysts to excel in their role and move farther in their scope of work and role. Multi-variable calculus, linear algebra, and statistics form a major part of machine learning. Some frequently used techniques are linear and logistic regression, decision trees, support vector machines, and Naive Bayes classification.
The skills required to be a data analyst can be learned online via courses and data analyst certification. If you’re a self-initiator you can take one subject at a time (as described above) and continue taking new courses as you learn, or take one comprehensive data analyst course that covers Excel to machine learning.
Pursuing a bachelor’s degree in data science, mathematics, statistics, economics, or related field will accelerate your pace of breaking into data analytics if you’re yet to do under graduation. To further etch your skills and add more credibility to your experience, taking vendor-neutral data analytics certification is a good move. This will open more job opportunities for you globally, given the surging demand for certified data analysts.
Top off your skills with the world’s leading certification. Here are some popular data analyst certifications that will add more value to your experience.
1. IBM Data Science Professional Certification – This data analytics certification is offered by IBM and hosted by Coursera. You will learn Python and SQL to analyze and visualize data and build machine learning models.
- DASCA Associate Big Data Analyst – This data analyst certification prepares you to undertake data analyst positions globally. You will learn social media analytics, mobile analytics, Hadoop, and R and Python programming.
3. edX Professional Certificate in Data Analysis – This certification is hosted by edX and offered by Microsoft. You will learn data analysis using SQL and advance techniques to perform operations on large data sets.
Get hang of real work
Data is sensitive to companies and employers seek entry-level analysts who come with hands-on experience and are adept at handling the large volumes of data easily. The next thing toward becoming a data analyst should be to gain practical experience. Gaining practical experience by working on projects and public data sets will build your portfolio and improve problem-solving skills, which is a crucial skill for data analysts. Several organizations allow access to public data for analysts to work on them. Github, Kaggle, Reddit, among others are a few companies that do so.
Becoming a data analyst has a clear path – learn, get certified, and have proven practical experience.