Since Data Analytics as a field has been pretty much talked about lately, there is hardly anyone who isn’t aware of its relevance in the present-day world.
However, the areas where it is being used currently remain largely unrecognised, and its potential remains highly underestimated.
Whenever we think about the applications of Data Analytics, how many scenarios can we enumerate?
OTT platforms suggest shows according to our taste, companies come up with new product suggestions, and Google shows us quite accurate search results. That’s it.
We might be aware of the industries in which Data Analytics plays a crucial role. But, can we exactly describe this role?
No, right?
Take the example of the healthcare industry. Data Analysts look at clinic attendance data to help hospitals and doctors predict when rush hours will hit so they can be ready for them.
Your local city hospital is a great example. Let’s say there have been complaints about long wait times making it difficult for patients to get the care they need. So Data Analysts use data about the hospital’s daily foot traffic to facilitate more informed decisions regarding how many doctors are needed on staff at any given time.
This leads to a reduction in wait times, improves patients’ experiences and makes better use of healthcare workers’ time too.
Take the case of one city zoo.
The City Zoo and Aquarium realised that on rainy days they were seeing huge drop-offs in attendance, but they had no way to accurately predict when those rainy days would hit.
This made staffing a real challenge. Some days they were overstaffed, and on other days they were unprepared for the rush of visitors.
As a solution, Data Analysts took years of weather records from the zoo and used that data to make accurate future weather predictions.
This made it easy for the zoo to estimate how much staff they would need and when.
Because the zoo could predict and manage its staffing needs more accurately, they were able to provide a better experience for visitors and dedicate more resources to creating a better experience for the animals too.
Thus, it can be concluded that Data Analysts aren’t only leveraged by top-notch companies and trending industries. Small businesses everywhere are also taking advantage of data-driven insights to better understand their customers’ buying habits, create more effective social media messaging, etc., to improve their operations and make better decisions.
This blog aims to provide a comprehensive understanding of the roles and responsibilities of a Data Analyst, the skills that they need to acquire, and how they can acquire those skills.
By the end of this article, you will have a crystal-clear picture of how to get started in this field, which makes you an asset to the company you will be joining in the future.
What is Data Analytics?
Data analytics is the process of examining raw data to uncover insights, patterns, and trends that can inform decision-making and drive business strategies. It involves various techniques and tools to collect, clean, transform, and analyse data with the goal of extracting valuable information and gaining a deeper understanding of a particular phenomenon.
Who is a Data Analyst?
Data Analysts collect, analyse, and interpret data to help businesses make better decisions. For this, they use a variety of tools and techniques.
They then use this information to identify trends, patterns, and insights that can be used to improve business performance.
Whether it's determining the most profitable product, identifying the latest market trends, or predicting future sales, a Data Analyst holds the key to unlocking the value in data.
What does a data analyst do?
In this section, we will look at the process followed by a Data Analyst within an organisation to come up with reliable findings that eventually form the foundation of the decision-making process.
This includes stages such as:
- Data Mining: Data Analysts collect data from various sources, whether they are internal (generated and collected within an organisation, such as sales records, customer databases, and employee information, and strictly meant for the use of the organisation only) or external (obtained from outside the organisation, such as social media trends, market analysis, and government records).
- Quality Assurance: Regardless of its size, data is the starting point for each and every business, in its day-to-day pursuits. Therefore, it’s the responsibility of the Data Analysts to ensure that the data collected is relevant to the business.
The next step towards achieving this is Data Preparation. - Data Preparation: This data, collected from various internal and external sources, is raw. This means it always has some errors, missing values, and redundancy. The job of a Data Analyst is to convert this raw data into structured form by removing the errors.
- Maintaining Databases: Petabytes of data get generated on a daily basis. The job of a Data Analyst is to create, keep updating, and delete databases as per requirements.
- Collaborating with other teams: Data Analysts decipher data and convey the hidden messages to other teams, who will use this information in the decision-making process.
- Preparing Reports: Data Analysts present their findings to other stakeholders in an easily understandable way. It can be in the form of charts or graphs that help others visualise the data and make data-driven decisions.
What are the skills required for data analyst?
According to the US Bureau of Labour Statistics, the number of job openings for Data Analysts is expected to grow by 23 percent between 2021 and 2031, significantly higher than the five percent average job growth projected for all jobs in the country.
But currently, there is a lack of skilled professionals. This can be an adversity for industries, but for aspiring Data Analysts, there is an opportunity in disguise.
Just mastering these seven skills will make you industry-ready in 2023 and enable you to embrace rapid career advancement in the high-growth field of Data Analytics.
Let’s take a look at them.
1. SQL
It stands for Structured Query Language. It is a programming language designed for managing and manipulating relational databases. It facilitates interaction with databases by writing queries to perform various operations such as retrieving data, updating existing data, inserting new records, and deleting records.
It is known for its simplicity and ease of use, allowing users to access and manipulate data stored in relational databases.
2. Statistical Programming
It refers to the use of programming languages to perform statistical analysis and manipulate data for research, data analysis, and decision-making purposes.
It involves languages such as R and Python (with libraries such as NumPy, SciPy, and Pandas).
These languages enable you to perform advanced analysis in ways that Excel cannot. Through them, you can clean, analyse, and visualise larger data sets more efficiently.
Though R was designed specifically for analytics, Python tends to be easier to learn, especially if you are a beginner.
3. Statistics
It is a mathematical branch of science that deals with the collection, analysis, interpretation, explanation, and presentation of data.
This pretty much sounds like what a Data Analyst usually does.
By leveraging statistical techniques, Data Analysts can identify trends and patterns in data, avoid biases, fallacies, and logical errors in the analysis, discover valuable insights, make data-driven decisions, and provide businesses and organisations with a competitive advantage.
It serves as the backbone of Data Analytics, ensuring that the test results are accurate, reliable, and meaningful.
4. Linear Algebra and Calculus
Just like statistics, these are other advanced mathematical skills that are non-negotiable.
Linear Algebra finds its applications in Machine Learning and Deep Learning, where it supports vector, matrix, and tensor applications.
Calculus is used to build the objective/cost/loss functions that teach algorithms to achieve their objectives.
Note: However, when it comes to mathematical skills, as you progress, you might realise that having a solid theoretical background isn’t mandatory.
Some seasoned industry professionals actually suggest taking the opposite route.
For example, in the 2019 article “Mathematics for Data Science”, data analyst Ibrahim Sharaf El Den advised taking a top-down approach.
“Learn how to code, learn how to use the PyData stack (Pandas, sklearn, Keras, etc.), get your hands dirty building real-world projects, use library documentation, and use YouTube or Medium tutorials,” he explains. “You’ll start to see the bigger picture, notice your lack of theoretical background, and actually understand how those algorithms work. Studying math will then make much more sense to you!”
5. Machine Learning
It is mandatory to clarify here that Data Analysts aren’t expected to have a command over machine learning algorithms but getting a mastery will give you a competitive advantage and set you on the path to becoming a data-scientist in future which is another lucrative career.
Machine Learning is largely about building algorithms aimed at finding patterns in large data sets. For this, the model is trained on a training set where it figures out the pattern and then given a test set on which it uses its learnings to predict the results.
The more data a machine learning algorithm processes, the smarter it becomes and the closer its predictions are to accuracy.
6. Data Visualisation
Data Analysts are a part of the decision-making process but have other people in the team as well who contribute significantly to it. In order to present their findings to all the other stakeholders and ensure that they make sense to them, it is essential to present the patterns decoded on a screen since not everyone is good with numbers and humongous chunks of data.
This is the other half of a Data Analyst’s role and responsibilities: to do convincing storytelling with their observations.
Learning data visualisation software like Tableau empowers you to transform your findings into dashboards, data models, and business intelligence reports.
7. Microsoft Excel
“Mention Excel to techies, and it’s often dismissed with a sniff” - Anne Walsh
Include Microsoft Excel in the list of Data Analytics skills to be acquired and see how aspiring analysts laugh it off with other significantly more advanced technologies to master.
Despite being clunky in comparison to other platforms, it is used by 750 million people worldwide.
Excel automates certain features and commands for better data analysis.
It has its own programming language, VBA, which, when deployed correctly, can save analysts a lot of time on frequently performed, repetitive projects such as accounting, payroll, or project management. Microsoft also developed its Analysis ToolPak with statistical modelling and data analysis in mind.
As the company’s help centre explains, “You provide the data and parameters for each analysis, and the tool uses the appropriate statistical or engineering macro functions to calculate and display the results in an output table. Some tools generate charts in addition to output tables.”
How to acquire data analyst skills: A step-by-step guide
In the previous section, we looked at the technical skills, you need to master to not just make a remarkable debut in the field of Data Analytics but keep thriving as well.
However, for a beginner, it’s not sufficient to list out the skills. What is needed is a roadmap giving them the approach to start this journey.
Let’s look at the step-by-step guide on how to acquire the skills.
1. Fundamentals: Start by building a solid understanding of data analytics, recognising its role in driving insights and informed decision-making across diverse industries.
Deep dive into its core concepts, including data types, variables, data structures, and the various types of data analysis approaches, such as descriptive, diagnostic, predictive, and prescriptive analytics.
2. Data Analytics Tools: Familiarise yourself with the wide range of data analytics tools. Master Excel for data manipulation and basic analytics, Python, R, and SQL, for data wrangling, statistical analysis, and database querying, and data visualization platforms like Tableau and Power BI to create captivating visual representations of your findings.
3. Statistical Concepts: Build a strong foundation in statistics by studying the essential topics.
Dive into descriptive statistics, learning to calculate measures of central tendency, dispersion, and shape of data distributions.
Explore inferential statistics, acquiring expertise in hypothesis testing, confidence intervals, and interpreting p-values.
Embrace probability theory, understanding the principles of randomness and uncertainty.
Delve into regression analysis, comprehending how to model relationships between variables and make predictions.
4. Data Collection and Preparation: Master the art of data collection and preparation to ensure data accuracy and reliability for effective analysis. Learn proven techniques for data gathering from diverse sources, including surveys, databases, and web scraping. Acquire expertise in data cleaning, identifying and handling missing values, outliers, and anomalies to enhance data quality. Address data transformation, normalisation, and scaling, ensuring data is in the most suitable format for analysis.
5. Data Visualisation: Explore a plethora of chart types, graphs, and interactive dashboards to effectively communicate complex data patterns and trends. Unleash your creativity to design visualisations that resonate with diverse audiences and aid in data-driven decision-making.
6. Exploratory Data Analysis (EDA): Delve deeply into Exploratory Data Analysis (EDA) techniques, unravelling hidden patterns, correlations, and outliers in data. Develop a keen eye for data exploration, employing graphical and statistical methods to gain meaningful insights. Leverage EDA to identify data distributions, relationships between variables, and potential avenues for further analysis.
7. Machine Learning Basics: Grasp the concepts of supervised learning, master classification and regression algorithms to make predictions and infer relationships between variables.
Unravel the nuances of unsupervised learning, understanding clustering and dimensionality reduction for pattern recognition and anomaly detection.
8. SQL and Database Concepts: Develop a profound understanding of databases and SQL for seamless data retrieval and manipulation.
Learn to create and manage relational databases, construct complex queries to extract relevant data, and perform data aggregation and transformation operations.
9. Real-world Projects: Immerse yourself in hands-on projects and real-world case studies, bridging the gap between theory and practice.
Apply your data analysis skills to diverse datasets, addressing real-life challenges and deriving actionable insights.
Showcase your expertise in solving practical data analysis problems and gain invaluable experience.
10. Embrace Continuous Learning: Explore the latest industry trends, emerging technologies, and best practices, positioning yourself as a proactive and adaptable data analyst.
In this data-driven age, becoming a proficient data analyst is a valuable skill that opens doors to exciting career opportunities. Though there are several modes and platforms to acquire technical skills like self-learning, YouTube tutorials, and online bootcamps, it is usually recommended that aspiring learners follow a rigorous and comprehensive training program under a reputed institute that not only equips them with the essential knowledge but also gives them hands-on experience through real-world projects to excel in the concerned field.
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Frequently Asked Questions
1. Does a data analyst need coding skills?
Yes. Data Analytics require coding skills. The extent to which they are required depends entirely on the technologies used by the company. However, some roles might be more inclined towards Data Visualisation, reporting, or interpretation. Having said that, getting accustomed to languages like R or Python will certainly keep you ahead of the competition. These languages help in data cleaning, data manipulation, and data visualisation.
2. Is data analyst in demand?
Yes, the demand for Data Analysts in India and around the world is high and is expected to grow in the coming years due to the increased use of data in decision-making processes within organisations.
3. How can a fresher become a data analyst?
Many people who are either fresher or are switching careers transition into data analytics. You just need to acquire the right set of skills. This is where NxtWave's Intensive Program comes in handy.
NxtWave’s Data Analyst Course offers a structured learning path that covers essential concepts, tools, and techniques with hands-on experience through real-world projects.
With NxtWave, you can learn directly from the alumni of IITs and professionals from top MNCs such as Microsoft and Amazon, work on real-time industry projects, and get doubt clarification from 9 a.m. to 9 p.m. every day. Regardless of your educational background, you can embark upon a high-paying Data Analytics job within five months of the commencement of the program.
