Important things to know
In the few moments your eyes have scanned the first words of this page, about 146,880Mb of new data was just turned out. This figure comes to 328.77 millionTb of data at the end of each day and climaxes at a breathtaking 120 zettabytes of data generated each year; that's enough data to fill 120 billion, one Terabyte hard drives. Mind-blowing, right?
Certainly, these are very large figures but where does all this data come from?
You're practically online nearly every day, texting on WhatsApp, checking your email, visiting websites, scrolling through social media and just surfing the web. Each of these actions, and the many unstated ones, that you take on the internet creates data. How much data do you think that would run into? A lot, right? Exactly.
Unfortunately raw data makes absolutely no sense to the naked or untrained eye. This is where Data Analytics comes in. Data analytics is, at its most basic, the process of interpreting disorganized data.
This article provides a practical guide in data analysis and how to become a data analyst in 2024.
What is the Role of a Data Analyst
Data analysts look for and share actionable insights by methodically examining data for links and patterns. Someone who can do this is called a data analyst. A data analyst is able to transform raw data into a powerful tool for problem solving, decision making and process optimization in several fields. Raw data is data that is non-contextualised, unrelated, disorganized in summary - meaningless. A data analyst is able to bring order to the chaos and harvest the goodies data has to offer.
This is particularly important because problem solving methods are only as efficient as the information at hand.
With data analysis future trends can be predicted and sense can be made of past experiences. Data analysis is fact-based and solution oriented so, it's the best shot at decision making compared to feelings or opinions.
The major responsibility of the data analyst then is to turn raw data into tailored information suited for best use; this means that you'll answer questions, solve problems, present your results and give advice to stakeholders and decision makers using the information.
The scope of the data analyst's responsibilities spans the stretch of data collection and acquisition to construction of data models, infrastructure and customization of reports.
The general outlook on the responsibilities of a data analyst follows;
- ✅︎ Problem statement
- ✅︎ Data collection
- ✅︎ Data sorting
- ✅︎ Data analysis
- ✅︎ Communication of results and reports
There are lots of tasks in between and some of the responsibilities include:
- ✅︎ Create and put into use data collection methods and databases.
- ✅︎ Prioritize company needs and define key performance indicators (KPIs) in close collaboration with management.
- ✅︎ Gather information from primary, secondary, and/or other sources.
- ✅︎ Sort and purify the data.
- ✅︎ Finding, evaluating, and deciphering patterns and trends in large, intricate data sets
- ✅︎ Present results visually to important stakeholders.
- ✅︎ Create and modify reports
- ✅︎ Create and keep up dashboards
- ✅︎ While data models, measures, and infrastructure are being established, create and update documentation about them.
The aim of all these activities is to bring out information from the hay stack of raw data and this advantage can be applied in almost every institution you can think of:
Data Analytics Application in Organisations
Data analysis can be carried out on customer's buying patterns, most sold goods and best sellers. A data analyst in this case will be able to find patterns in the spreadsheet of several dates of purchases and pinpoint periods of highest patronage for a particular item, with this, the company can know when to mass produce or restock or intensify advertisements or introduce discounts and on and on.
Social media companies can monitor user engagement through data analysis to curate user specific content, tailored advertisements and generate more revenue.
Data Analytics Application in the Healthcare Industry
In the health industry, data collected from surveys can help health agencies determine the rate of spread of certain diseases, causative factors of some illnesses and identify susceptible populations. This can be used to outline disease eradication and vaccination strategies with a timeline. Data analysis is also used to improve quality of treatment in hospitals and optimize patient care and medication therapy.
Data Analytics Application in Legal Practice
Law makers, decision makers and stakeholders in government need data analysis in cases where the effects of a policy on particular demography needs to be studied so there can be modification to those policies or development of entirely new policies in which case data analysis would inform the outcome.
What Are the Core Skills for Data Analysts
In any job, skills like a firm knowledge of the business and understanding of the terrain, communication, critical thinking and problem solving skills will always come in handy but as a data analyst you'll need to have skills specifically required for this line of work, known as core skills, to land your first job and be successful at it. The following is a compilation of core skills you must possess to be a data analyst.
Proficiency in Programming Languages and Software Applications
It's impossible to evade programming and coding in your journey as a data analyst. That's because many software and tools that you would need to deep mine for data, sort data and analyse data usually require codes to be written or tweaked for them to run as desired. Examples of these software include Tableaux, Excel etc. Popular programming languages you need to learn include Python and SQL.
Statistical Analysis and Data Interpretation
You would need to have a good background in statistical analysis and this involves using mathematical techniques to perform statistical operations. This is the foundation of your job as a data analyst as you'd also need to know what statistical model is most appropriate in analyzing data or interpreting it. A background degree in applied math, computing or statistics will come in handy.
Examples of statistical concepts for data analysts include the following
- ✅︎ Regression analysis: a statistical technique for understanding how a dependent variable and one or more independent variables are related.
- ✅︎ Descriptive statistics: a statistical method used to describe the outlook and basic features that characterise a particular set of data.
- ✅︎ Probability distribution: a technique that predicts the degree of likelihood of all possible outcomes and results.
- ✅︎ Central tendency: this technique highlights the centrality of a data set and determines the central value in a data set. Mean, Median, Mode are common measures of central tendency.
- ✅︎ Hypothesis testing: A statistical method useful for making and evaluating the likelihood of predictions concerning the outcome of a data set.
These statistical concepts and techniques have application in finance, clinical trial design, political campaigns, weather forecasting, business management, quality control and much more.
Tools and Technologies Every Data Analyst Should Know
Data analysis tools and technologies are the work tools data analysts use to perform their carry out tasks and get jobs done. As an aspiring data analyst you must acquire technical skills in them.
Some of the most popular and essential tools you must know how to use include Rapid Miner, Excel and Tableau
- ✅︎ Rapid Miner: operating on R and Python, Rapid Miner is a software platform for preparing data, executing machine and deep learning, deploying or testing predictive models and mining text.
- ✅︎ Excel: a popular and basic data visualization analysis tool that operates on app-specific instructions. You may not need Python and R to operate Excel but it is limited in functionality range.
- ✅︎ Tableau: tableau is a really good tool for data visualization and business intelligence that can handle bulky raw data. It also affords the opportunity of sharing flow charts, reports and all.
Choosing the Right Tools for Data Analysis Projects
There are several tools available for data analysis and some are better suited for some projects than others. You'd need to know how to choose what tool is best for your project. Here are guidelines you can follow:
- ✅︎ Understanding your data, a holistic view of your data is really important.
- ✅︎ Define your goals, set out what you want to achieve at the end of analysis
- ✅︎ Choose your methods, decide which method is best suited to analyze your data
- ✅︎ Choose your tools, select the tools that operate such methods and you're proficient in.
Conclusion
In the fast-evolving domain of data analysis, mastering technical skills is the compass that guides your journey. This comprehensive guide has illuminated the intricacies, from the importance of data cleaning to crafting an engaging portfolio, networking effectively, and excelling in interviews. As you embark on your data analyst journey in 2024, remember: each skill mastered, each connection forged, and each challenge overcome is a step towards realizing your full potential in the data-driven landscape.
