Data analytics is one of the hottest career choices in 2023. When someone wants to get into a tech role from a non-tech background, data analytics is one of the first jobs they want to pursue. However, choosing a career is a complex process and people have many doubts about how to proceed ahead. In this article, we will discuss if data analytics is hard to learn. We will also discuss tips and tricks to help you get into data analytics easily.
With the digital footprint of every person on Earth increasing day by day, we are producing more data than ever before. The data that we produce while surfing social media, shopping online, or using different software applications often have huge economic value for companies. To derive valuable insights from the data, companies often employ data analysts.
Data analysts are required to use the available data and derive insights to answer business questions. Looking at the amount and variety of data, one might think that data analytics must be hard as you need to process a lot of data to produce insights. Well, that’s not completely true. To answer if data analytics is hard to learn, let us first discuss what data analytics is, what skills are required to become a data analyst, and what tools and techniques you need to ace a data analytics job.
What is Data Analytics?
Data analytics is the process of analyzing data to derive business insights in order to assist the decision-making process in an organization. There are various types of data analytics processes that we use in different tasks like analyzing customer behavior, predicting future trends, or performing cause and effect analysis in an organization. Generally speaking, we can classify data analytics tasks into four parts.
- Predictive data analysis: As the name suggests, predictive data analysis is used to predict future events based on historical data. We use predictive analytics to forecast prices, inventory, sales, profits, customer churn, employee churn, etc. It is also used to predict macroeconomic factors such as stock market prices, housing prices, the GDP growth rate of countries, etc.
- Diagnostic data analysis: Diagnostic data analytics is used to answer questions by cause and effect analysis. In diagnostic analytics, we use historical data to answer questions or find solutions to a particular problem by identifying patterns and dependencies in the data.
- Prescriptive data analysis: Prescriptive data analysis is used to analyze data and prescribe optimal solutions for decision support, resource allocations, supply chain optimization, etc.
- Descriptive data analysis: We use descriptive data analysis to describe or provide a detailed summary of past events using data. Descriptive data analytics is used in tasks like customer segmentation, financial analysis, etc.
In all the above data analytics domains, we need almost similar skills. Let us discuss the skills you need to learn to become a data analyst.
Skills Required to Become a Data Analyst
In a data analyst job, we need to perform different types of analysis tasks that require varying skills. Following are some of the skills you need to learn to become a data analyst.
- Statistical Analysis: For analyzing data, you need to understand fundamental statistical concepts and techniques. This includes hypothesis testing, regression analysis, probability, sampling, etc. We need to apply statistical methods to analyze data and draw useful insights. Without knowing statistics, you can’t be good at data analysis.
- Data Cleaning: The data which is made available to a data analyst is never ready to use. You need to learn techniques to clean and preprocess data. You need to learn how to handle missing values, detect outliers, data imputation, and feature engineering. You also need an understanding of data quality and data validation techniques to prepare the data for analysis tasks.
- Data analysis: For analyzing data, you need to learn techniques like forecasting, factor analysis, cohort analysis, cluster analysis, regression analysis, time series analysis, etc. These techniques are mostly an amalgamation of data mining, statistical analysis, and machine learning techniques.
- Data visualization: After data analysis, you need to present the findings to the management and other stakeholders so that they can take decisions using the findings. Thus, you need to have a good understanding of different data visualization techniques. You also need to have a good hands-on experience with one of the data analysis tools such as Power BI, Tableau, Looker Studio, SPSS, etc.
- Storytelling: In a perfect world, charts, and visualizations should not need any explanation. However, you will need to explain your findings to the stakeholders in most of the cases. Hence, you need good oratory skills to clearly convey your findings to the management.
- Machine learning: Data analysts don’t need to understand how machine learning algorithms are implemented. However, they often need to use machine learning models for analyzing data. Hence, it will be good if you know how machine learning algorithms work. This will help you apply the right machine-learning techniques to the right types of data.
- Query Languages: Most companies store their data in relational databases like MySQL, Oracle, MS SQL, etc. To work with the data, you need to learn SQL. Nowadays, no-SQL databases have also gained popularity. Hence, you need to learn how to work on the database management system that your company uses.
Is Data analytics Hard to Learn?
Data analytics isn’t hard to learn. In fact, it is one of the most popular areas people choose to start with when they switch from a non-technical career to a technical one. Why? Because data analytics is easy to learn.
For an entry-level role as a data analyst, you just need to know statistics, SQL, and a data analysis tool like Tableau. That’s it. Knowing even these three things can land you a data analyst internship. For a full-time job, you need to have a good understanding of data analysis techniques, machine learning, etc. Learning these skills may take time. However, none of the skills I mentioned above are very hard to learn. Hence, data analytics is very easy to learn.
If you want to get into data analytics, you can easily prepare for the role in a maximum of 6 months. Companies like Google, Microsoft, and IBM provide curated learning paths with data analytics certifications on platforms like Coursera to help you get started with data analysis. This makes it easier for you to get started with data analytics.
Is Data Analytics Easier Than Programming?
No data analytics isn’t easier than programming. If you want to learn programming and get into a software development job, you need to learn data structures, system design, object-oriented methodologies, etc. You also need to design new algorithms to solve new problems. This is not the case in data analytics. In data analytics, you just need to use existing tools and techniques to analyze data. In my opinion, using existing tools, techniques, and algorithms is always easier than creating new software and designing new algorithms. Hence, data analytics is easier than programming.
Is Data Analytics a Good Career Choice?
Yes. Data analytics is a great career choice if want to switch from a non-technical job role to a technical one. After starting as a data analyst, you can easily advance your career into roles like machine learning engineer, data scientist, or even data engineer by learning new skills as per needs.
In entry-level data analyst roles, you can get $80,000 to $10,000 per annum. As your career will advance into data science or machine learning, you can even fetch more than $200,000 in salaries. Hence, data analytics is a good career choice if you want to get into data science or machine learning domain.
Is Data Analytics The Same as Data Science?
No, data analytics isn’t the same as data science. Data analytics primarily focuses on analyzing data using existing tools and techniques to gain insights, trends, and patterns. On the contrary, data science encompasses a broader set of skills and techniques that involve collecting, processing, analyzing, and interpreting large volumes of data to extract meaningful insights.
- As a data analyst, you just need to use various statistical and analytical techniques to explore data, identify correlations, and derive actionable insights. On the other hand, you need to use statistical modeling, machine learning, programming, and domain expertise to solve complex problems, make predictions, and uncover patterns in data as a data scientist.
- In a data science role, you need to understand the implementation of machine learning algorithms. You might also need to implement new algorithms to solve business problems. In a data analyst role, you just need to apply existing machine-learning algorithms to data. To learn more on this topic, you can read this article on data analyst vs data scientist.
In this article, we discussed if data analytics is hard to learn. We also discussed the skills required to become a data analyst. To learn more about different careers, you can read this article on data science vs computer science. You might also like this article on how long it takes to learn SQL.
I hope you enjoyed reading this article. Stay tuned for more informative articles.
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