Becoming a data analyst is becoming more popular as data generation increases. Specific skills are required to become a data analyst, and specific steps must be followed. This article provides an in-depth understanding of what is needed to become a data analyst, including the necessary skills and actions to achieve this goal.

If you are interested in pursuing a career in data analytics, this article provides a comprehensive guide for understanding the field of data analytics. It offers a brief overview of data analysis prospects, the different types of data analytics jobs, and opportunities for those new to the field.

What is Data Analytics?

Data analytics (DA) is analysing large data sets with the help of specialised tools and software to draw meaningful insights and conclusions. DA is widely used across various industries, such as business, finance, healthcare, and more, to inform strategic decisions and improve organisational performance. Additionally, DA techniques are also utilised by scientists and researchers to test and validate scientific models, theories, and hypotheses. 

DA aims to extract valuable information from data, which can be used to make informed decisions, improve processes, and gain a competitive advantage. Data analytics can be applied to various data types like structured, semi-structured and unstructured data. The data can be analysed using descriptive, diagnostic, predictive and prescriptive analytics methods. 

The use of DA is increasing rapidly with the growth of big data, and it is becoming a vital tool for organisations to understand their operations and customers better.

Skills Required for Data Analytics

• Strong mathematical and statistical skills.
• Familiarity with programming languages such as Python, R, SQL, and SAS.
• Knowledge of data visualisation tools such as Tableau and Power BI.
• Experience working with databases and large data sets.
• Strong problem-solving and analytical skills.
• Understanding of machine learning concepts and techniques.
• Strong communication and presentation skills to effectively share insights with stakeholders.
• Familiarity with industry-specific tools and technologies.
• Knowledge of data governance and security.
• Experience working in a team-oriented environment.
• Strong knowledge of data cleaning and preprocessing techniques.

Roles and responsibilities of Data Analytics

• Collecting, cleaning, and organising large data sets from various sources.
• Analysing and interpreting complex data using statistical and quantitative methods.
• Identifying patterns and trends in data to inform business decisions.
• Developing and implementing data models and algorithms to optimise statistical efficiency and quality.
• Creating visualisations and reports to communicate insights to stakeholders.
• Providing recommendations to improve business operations and performance.
• Collaborating with cross-functional teams to identify and solve business problems.
• Staying up-to-date with industry trends and new technologies to improve data analysis techniques.
• Developing and maintaining databases and data systems to ensure data quality and accuracy.
• Monitoring and maintaining the security of sensitive data.
• Building, testing and deploying machine learning models.
• Managing and mentoring junior data analysts.

Data Analytics Qualifications

Data analytics qualifications typically include a combination of education and experience in statistics, mathematics, computer science, and business. Some familiar qualifications for a career in data analytics include the following:

• A bachelor's degree in a field such as statistics, mathematics, computer science, economics, or engineering.
• A master's degree in an area such as data science, business analytics, or a related field.
• Advanced certifications in data analytics or related fields, such as the Certified Analytics Professional (CAP) or the Certified Data Scientist (CDS).
• Strong programming skills in Python, R, SQL, and SAS.
• Experience with data visualisation and reporting tools like Tableau, Power BI, or Excel.
• Experience with machine learning and artificial intelligence techniques.
• Knowledge of big data technologies like Hadoop, Spark, and NoSQL databases.

Careers in Data Analytics

Data Analysts

A Data Analyst collects, cleans, and organises large data sets from various sources. This may include data from internal systems, external sources, or publicly available data sets. Once the data is collected, the Data Analyst will use various tools and techniques to clean and organise it, ensuring it is accurate, complete, and ready for analysis.

The Data Analyst will then use statistical and quantitative methods to analyse the data and identify patterns, trends, and insights. This may include using data visualisation tools to create interactive visualisations and reports that allow stakeholders to understand the data quickly. The Data Analyst will also use statistical modelling techniques to predict and forecast future trends.

Business Intelligence Analysts

A Business Intelligence (BI) Analyst is responsible for using data to inform business decisions, create visualisations and reports, and identify patterns and trends in data. This role involves gathering data from various internal and external sources, analysing it, and providing stakeholder insights and recommendations.

They analyse data to identify cost savings, process improvements, and revenue growth opportunities. They also help identify risk areas and develop strategies to mitigate them. They work closely with cross-functional teams to identify and solve business problems and provide valuable insights to support decision-making.

Data Scientists

A Data Scientist is responsible for using advanced statistical and machine-learning techniques to extract insights from data and inform business decisions. This role involves collecting and cleaning large data sets from various sources, analysing the data using complex algorithms and models, and providing insights and recommendations to stakeholders.

Data Scientists use various techniques, such as descriptive, diagnostic, predictive and prescriptive analytics, to extract insights from data. They use statistical modelling techniques to make predictions and forecast future trends and machine learning algorithms to identify patterns and insights that may not be immediately obvious from raw data. They also use techniques like Natural Language Processing, image processing, and speech processing to analyse unstructured data.

Data Engineer

A Data Engineer is responsible for designing, building and maintaining the data infrastructure that enables organisations to collect, store, and process large data sets. This role involves designing and building data pipelines, storage solutions, and warehousing systems.

Data Engineers work closely with Data Scientists and Analysts to ensure that data is collected, stored, and made available promptly and accurately. They design and implement data pipelines that collect, process and move data from various sources to storage systems, such as data warehouses, data lakes, or cloud storage. They also design and implement data storage solutions that are scalable, robust, and secure.

Supply Chain Data Analyst

A Supply Chain Data Analyst is responsible for using data analytics techniques to optimise supply chain processes and improve the efficiency of logistics and distribution operations. This role involves collecting and analysing data from various sources, such as ERP, warehouse, and transportation management systems, to identify patterns and trends that inform supply chain decisions.

In addition to these fundamental duties, supply chain data analysts must keep up with market developments and emerging technology to advance data analysis methods. They are also a part of data governance, ensuring data is used sensibly and following applicable laws and standards.

Salary of Data Analytics