Data Analyst course Malaysia is one of the trendiest courses in the 21st century! How does one become a data analyst?
Online, data analytics is a hot topic. It’s not always possible to discover a straightforward definition of a data analyst’s day-to-day duties. To make matters worse, data analytics is commonly confused with data science, machine learning, artificial intelligence, and business analytics. While data analytics is used in all of these domains, it is a separate discipline.
Why data analytics?
Data analytics is the process of making sense of chaotic data. Data analysts aim to identify and share relevant insights by carefully studying data for patterns and correlations. But what is data? Anything you can think of. Data are often numerical (quantitative data). But data may also be sounds, pictures, phrases, or anything else that can be understood (qualitative data).
An analyst’s work starts with ‘raw data.’ Without context, raw data are worthless. When the chaos is controlled, we can get important information from them. So data collection, cleansing, and organisation are all part of data analytics.
Aside from that, good data analytics combines several strategies. Statistic, programming, visualisation, etc. Luckily, many of these processes have been automated to save time. Some are even forming their own fields. But a smart data analyst will know them all.
Want to know more about data analytics and data analysts? This and other topics are covered in our free beginner’s short course. See tutorial one: A primer in data analytics.
The value of data analytics
Data analytics is important for two reasons. First, it aids decision-making. The second is scientific. These two features make data analytics a powerful tool. Making judgments based on data rather than opinion or ‘gut feel’ is a far more scientific approach. However, data analytics is by far the strongest instrument we have for forecasting future patterns and drawing inferences about previous occurrences.
Data analytics has many uses in society. We hear a lot about data analytics being used to anticipate future sales, or to guide product development and marketing expenditure. But data analytics is about much more than just increasing profits. As a result, patients get better treatment. It’s now being used in agriculture to change global food supply. Governments utilise it to combat concerns like human trafficking. So, if you want to help better both the world and company, data analytics may be for you!
2. What is a data analyst?
Now that we understand data analytics, let us examine the job of the data analyst.
It’s your job as a data analyst to convert raw data into valuable insights. Using data and the insights it gives, you can solve particular issues or answer specific questions. You’ll next share these findings with important stakeholders and decision makers so they can act or plan appropriately. Data analysts may also be in charge of monitoring entire data collection and storage operations, as well as creating data quality standards.
The activities and responsibilities described in data analyst job descriptions are an excellent method to measure what a data analyst really performs on a daily basis. Based on Indeed.com job descriptions, a data analyst may anticipate to accomplish the following:
- Create databases and data collecting systems
- Work closely with management to identify key performance indicators and business requirements.
- Collect primary and secondary data sources
- Clean up data
- Identify, analyse, and comprehend complicated data patterns
- Present results to important stakeholders
- Customize reports
- Maintain dashboards
- Document the development of data models, measurements, and infrastructure.
So far, we’ve looked at a data analyst’s job from a high level. Now let’s focus on some of the more particular data analysis jobs.
3. How does a data analyst work?
Your role as a data analyst is to use data analytics to discover and solve problems. Depending on your professional path, you may opt to specialise in data visualisation or data engineering. For a newbie, it’s necessary to learn the full procedure.
So, what should a data analyst anticipate to do? The major duties are:
To analyse data, follow these five steps: define the question, collect data, clean it up and evaluate it.
- Making a point
- Getting data
- Data purge
- Analyzing
- Result communication
Making a point
First, establish your goal. This is the most difficult phase of the procedure. Because a seemingly evident issue may not always get to the root of a problem.
Assume you work for a corporation that wishes to increase sales. Senior management intends to achieve this by creating a new product line. So you spend a lot of time and money deciding what things to develop and where to sell them. The company’s present goods are OK, but the sales process is flawed, resulting in low customer satisfaction and little repeat business. With this knowledge, you may discover that sales training increases revenue at a cheaper cost.
It’s important to look at a problem from many perspectives before devoting too much attention to it. It also means speaking truth to power (in this case, telling managers that their new product idea is wrong). Defining the question requires a thorough grasp of the business’s requirements and expectations, as well as measurements and KPIs. Usually, first analyses are done at this point.
Getting data
After identifying the question, determine which data are most useful in answering it. The data might be quantitative (such as marketing statistics) or qualitative (such as customer reviews). First-party data (collected directly by you or your organisation), second-party data (collected by another organisation), and third-party data (which is aggregated from numerous sources by a third-party).
If you don’t already have this data, you’ll need to gather them. Surveys, social media monitoring, website analytics, internet tracking, etc. Once you have the data, you may clean it up.
Data purge
New data is generally in raw format. It hasn’t been sorted, proofread, etc. The data must be cleaned before it can be analysed. Custom algorithms, generic software, and exploratory analysis are used to improve it.
Examples of data cleaning jobs include fixing mistakes, duplicating, and outliers, deleting unneeded data (that doesn’t help your research), reorganising the data, and filling in gaps. Then you’ll verify the data. Check that it fits your criteria. It doesn’t always work, so you have to go back a step. So data cleansing is an iterative process. Data wrangling is the process of gathering and cleansing data. This guide explains data cleansing.
Analyzing
Once your data is cleaned, you’re ready to evaluate! There are various sorts of data analysis, and determining which is ideal for the job at hand is difficult. To make things easy, we’ll summarise the four major data analytics areas.
Data analysis might be descriptive, diagnostic, predictive, or prescriptive.
Descriptive analytics is one. To better comprehend a dataset, summarise (or describe) its characteristics. It isn’t frequently utilise to reach solid conclusions, but it is a good starting point for additional research.
Next, diagnostic analytics seeks to explain events (e.g. by exploring correlations between values in a dataset). This is utilised at the initial step of data analytics, defining the question.
We also offer predictive and prescriptive analytics (which assist discover patterns based on historical data) (which helps decide on a future course of action). This latter is sometimes done via machine learning.
Result communication
The last stage is to report your findings to individuals who commissioned your work. This generally entails developing graphs and charts to represent your data. More on data visualisation here. In addition, it may require designing interactive dashboards. It’s easy to ignore this step’s artistry, but it’s critical. You must not only evaluate your results accurately, but also communicate them clearly to non-technical persons. Insights of high quality and well-understood are used to make decisions.
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