Heaps of data are produced every day, every second. This data needs proper management to bring it to use. Most in-demand and invaluable people in the world today are Data Analysts. They are sought after across industries like healthcare, finance, IT, etc. The decision-making in any business scenario depends on the valuable insights given by the Data Analysts by collecting and analyzing the data and applying appropriate algorithms to help reach the right decision after a statistical analysis. Data grows exponentially, and to manage it, after a study of trends and patterns, top Data Analyst Skills come to the rescue.

List of best data analyst skills

Data Analysis – History and Future

Data analysis is older than the Internet. Data analysis earlier supplemented the process of decision-making. With technological advancement, data analysis will control business decisions. Ultimately, it may even balance the technological rise.

What is Data Analysis?

Data Analysis is the process of collecting large amounts of data from numerous sources to excerpt valuable insights and make informed decisions. It is done by applying algorithmic processes to find patterns and trends. The aim is to have actionable conclusions to improve business consequences.

The objective of data analysis is to organize data rationally. With globalization, every business wants to have a global presence. Knowledge of demand, competition, available resources, etc. is essential.

Data analysis is the technical study of available data from collated unstructured information to draw useful understandings, improve efficiency, increase productivity, and make business decisions. Data analysis involves six stages – Data discovery, preparation, Planning, the building of data models, communication of outcomes, and operations.

Steps in Data Analysis are – collaborating needs, creating a questionnaire, producing data, Setting KPIs, ignoring useless data, conducting statistical analysis, building data management, and Integrating technology.

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Data is Evaluated Based on Quantitative & Qualitative Analysis –

  • Quantitative data is numerical information. E.g.: pricing, earnings, sales reports
  • Qualitative data is the information required for meaningful assessment – principally expression in the form of words. E.g.: inspections, interviews, or research.

Data Analysis vs Data Analytics –

It can be a misconception to many that Data Analysis and Data Analytics are the same. Data analysis is done to examine the data to understand past events in connection with the data whereas data analytics is done to find what can happen with the data in the future.

 

Why is Data Analysis Important?

Data analysis incredibly impacts the functioning of any organization. Some important advantages of data analysis across industries are:

  • It can discover information critical to achieving a business goal
  • Helps in identifying probable customers
  • Organizations can understand their market position in comparison to their competitors

Is Data Analysis a Good Career Option?

Data Analysis has become a necessity in not just IT but all sectors like e-commerce, Manufacturing, Telecommunication, Banking, Finance, and Consulting. Even Government organizations are using data analysis tools to make precise decisions. Demand for Data Analysts has gone up across sectors.

Due to digitalization, companies strive to devise a methodology to manage the data generated constantly. Data Analysts provide valuable insights to benefit businesses. Starting a career as a Data Analyst is a rewarding career option as there is a growing demand for experts in Top Data Analyst Skills.

The salary is comparatively higher and competitive, the opportunities for data analysts are varied, and in almost all fields, the work of data analysts is exciting and challenging Top Data Analyst Skills can be applied to all industries and in any normal day-to-day working.

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Who is a Data Analyst?

Locating, exploring, collecting, processing, and performing analysis of large datasets is done by Data Analysts. All organizations collect data in some form or the other, viz customer feedback, surveys, reviews for products or services, accounts, marketing research, etc.

A data analyst takes this data to figure out several measures, to improve customer experience, pricing, reduction in costs, etc. Working and preparing a Tableau dashboard, coding to illustrate patterns, and presenting the findings and recommendations.

Who Should Learn Top Data Analyst Skills?

This versatile field can be beneficial to many professionals.

  • Business professionals: To achieve better decision-making and improve performance by gaining insights into client conduct, trends and patterns in market demeanor, and operational value.
  • IT professionals: To improve the performance of IT systems, detect security threats, and enhance infrastructure.
  • Marketing professionals: To improve promotion drives, and measure the efficacy of marketing efforts.
  • Operations professionals: To improve SCM (supply chain management), improve industrial processes, and cut costs.
  • Finance professionals: To identify trends in fiscal data, and make improved investment decisions.
  • Healthcare professionals: To improve the performance of healthcare systems by reducing costs.
  • Researchers: To analyze, identify patterns, and make new findings.
  • Entrepreneurs: To find a new business opportunity

 

Who Can Be a Data Analyst? 

STEM (Science, Technology, Engineering, and Mathematics) qualifications and training are necessary to acquire the basic skills for this profession.

Fresh graduates: Aspirants from engineering, science, or commerce backgrounds can become data analysts with the help of certification. Data Analysis Certification will help you acquire the required skills and the advantages of a data analysis certification are:

  • Improve the repertoire of the skills in your portfolio
  • Learn the advanced analytical techniques and processes crucial for a candidate with Top Data Analyst Skills e.g., data mining, cleansing, and processing
  • Attain proficiency in popular data analysis tools and software in business usage

MBA/Finance/Engineering graduates: The data analyst role can be a good fit for MBA/finance/engineering graduates post learning the Top Data Analyst Skills.

Software Developers, IT Analysts, and Test Analysts: Moving to the roles of data analysts is easier for this lot as skills, including knowledge of programming languages, tools, and methods are common. Professional certification in data analysis can be an extra edge.

System/Database Administrators: With the requisite skills these people can work as Data analysts as most companies are making use of data to make business decisions.

Non-experienced professionals: Recruiters can hire an inexperienced candidate as a junior data analyst or an entry-level professional. Candidates taking up a certification course can switch to higher-level positions.

Experienced professionals: It is comparatively easier to find a job in the Data Analysis field for an experienced professional.

Qualifications and Pre-requisites –

Strong mathematical skill is a primary requirement to be able to build a career as a Data Analyst. Familiarity with statistical software packages such as SAS, R, or SPSS. Excellent communication skills for effective presentation. A full-time, four-year bachelor’s degree course in statistics, computer science, or mathematics is necessary to become a data analyst. The certification course will be an added advantage to becoming a Data Analyst.

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Top Data Analyst Skills

They need specific technical skills to succeed in their field but, those in this profession also need some other skills listed below. We take you through varied skills broadly classified into two categories viz; Technical and Non-Technical

 

Technical Skills –

  • Domain Knowledge: Understanding how things are done in a particular industry or organization will enhance your domain knowledge. It is to be learned and developed on the job. However, an added advantage is a 30-foot overview of a particular industry you wish to work in. The person should know what he or she needs to analyze, to be able to do the job efficiently. So, domain knowledge is one of the Top Data Analyst Skills.

 

  • SQL (Structured Query Language): In the data analysis area, SQL is the standard and formal database language. It allows to update and collect specific data. It’s used to process large databases that can’t be managed by spreadsheets. Therefore, most data analyst jobs require a knowledgeable SQL person. The language is an advanced version of Excel and can handle large datasets that are beyond the purview of Excel. Almost all organizations need someone who knows SQL either to manage and store data, or build or change database structures. Writing complex queries to extract data depicts proficiency in SQL. Major job postings require SQL skills as one of the Top Data Analyst Skills to study.

 

  • Spreadsheets: Despite advancements like SQL, spreadsheets remain the go-to tool because of their simplicity, ease of use, and most importantly extremely useful. Over the years, organizations across different industries have preferred reports on spreadsheets. There are lot many things to learn and uncover under advanced Excel methods like writing Macros and using VBA lookups etc. Excel is used by approximately 75+ crores worldwide. “Excel skills” find their place in the qualifications section of the job postings. Excel is automating some features for data analysis. Excel has VBA as its programming language, which is used to create macros. VBA is time-saving if deployed correctly, for repetitive tasks such as accounting, and payroll.

 

  • R & Python: Any task that can be performed by Excel can be performed by R and Python in a better and faster way. They are programming languages acceptable as per industry standards that are used to perform advanced analysis on a larger chunk of the data. R and Python are built specifically for analysis and are both open-source and free. R is the preferred language and has ranked fifth amongst the top 10 programming languages in 2019. Any aspiring data analyst should learn Ror Python on priority. Python’s applicability to Artificial Intelligence development is important.

 

  • Machine Learning: Though proficiency in Machine learning is not a mandate, machine learning has now become an important expansion in data analytics. Building algorithms to identify big data sets and process the data gives a competitive edge. Not every analyst is required to work with machine learning but having a fair idea is important to get ahead in the field.

 

  • Data Cleaning Skills: Data Analysts should possess data cleaning skills as a primary skill which includes quality issues, and missing or duplicated data, and then fix these issues through cleaning techniques.

 

  • Data Visualization and Reporting Skills: Data analysts generate reports to summarize the findings, and briefly present them with clarity for easy understanding. Analysts present data in eye-catching, high-quality charts graphs, and illustrations to make impactful understanding at a glance to help organizations make better decisions. Data visualization has become a necessary skill. Proficiency in using tools such as Tableau, Power BI, and QlikView enables data analysts to create easy-to-understand, meaningful insights, interactive dashboards, charts, and graphs to visualize dat

 

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Non-Technical Skills –

  • Problem-Solving and Critical Thinking: Data analysts should be able to analyze data identify patterns and extract information through in-depth thinking. Data Analysts may run into different kinds of problems in their day-to-day work but being able to find innovative solutions to the problems with limited available resources is an essential skill for a data analyst. Using data to find solutions or alternative solutions to your problems can sometimes be quite tricky. Data analysts need to evaluate the pros and cons of each approach while making complex-looking data in simpler versions.

 

  • Attention to detail: Identifying the smallest hints hidden in the data is a key skill in Data Analyst. This especially is helpful while setting up the right processes for sorting data. Data analysts should be particular and have strong attention to detail by ensuring data is precisely entered, cleaned, and evaluated as trivial errors can have substantial effects. They should be able to identify errors in the data and ensure accuracy and data quality after cleaning and processing.

 

  • Time Management Skills: To meet result-oriented deadlines with quality in the work time management skills are of high importance. It involves task prioritization, setting realistic timelines, and communicating expectations. Prioritization of the task based on its importance and urgency is an added skill.

 

  • Data Security and Privacy Skills: Data analysts should devise methods to implement best practices to protect sensitive data.

 

  • Continuous Learning: The data analysis field is constantly developing, so data analysts need to keep abreast with new tools, techniques, and best practices. This can be done by attending conferences and engaging with the data analysis community.

 

  • Research: Data analysts don’t always get the data readily available for the next set of actions, they have to research to collect the information. For the interpretation of the raw data, data analysts put efforts into additional research so that the findings can be articulated in an easy-to-understand format. Also, data analysts research the latest industry norms to process data better.

 

  • Communication Skills: Collaboration with your colleagues or clients becomes next to impossible without communication skills. Explaining complex data and patterns to a non-technical person requires excellent oral communication skills. Writing reports about the findings and recommendations requires exceptional written communication skills. Presenting data and analysis results by communicating it effectively helps organizations make the right decisions.

 

  • Business Acumen: A strong understanding of the industry, the business, its products and/or services the company’s goals and objectives, its competitors, the challenges that the company is currently facing, and the difficulties it might face in the future is very important to effectively identify areas where their inputs can add value and support business decisions.

 

  • Team Player: Data analysts work with different people from different levels to get the job done and to guide the company in achieving its goals and objectives.

 

  • Statistical Analysis and Modelling Skills: Regression analysis, hypothesis testing, and clustering are the techniques used to analyze data and discover trends and patterns. Experience with predictive modeling, using statistical techniques aids in making predictions about future events.

 

Types of Data Analysts –

  • Quantitative analysts: They use Excel, Python, and R to analyze and find patterns within large datasets
  • Market research analysts: They guide the organizations to understand the market they function in
  • Business analysts: They use data to analyze business processes and assess the business model
  • Business intelligence analysts: They use large historical datasets to regulate what happened in an organization
  • IT systems analyst: They use data to create and maintain systems.
  • Operations research analysts: They use data to decide about operations.
  • Medical and healthcare analysts: They use data to analyze medical results and advance patient care.
  • Intelligence analysts: They use data to analyze patterns affecting a nation’s security, like terrorist movements.

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Typical Work Day in the Life of a Data Analyst –

  • Extracting the data using SQL skills
  • Analyzing the data using programming skills
  • Reporting the findings using communication skills

 

 Different Responsibilities of a Data Analyst Include –

  • To work in collaboration with IT, and management teams
  • To regulate organizational goals
  • To dig data from sources
  • To clean the data and discard unimportant information
  • To understand results using typical statistical practices
  • To highlight trends and patterns in data sets and forecast
  • To plan improvement in the process
  • To guarantee data visualization
  • To design, create, and maintain a relevant and useful database
  • To prioritize tasks
  • To create dashboards, graphs, and visualizations
  • To maintain the database

 

A Few Questions and Suggested Answers That May Help You Crack the Job Interview From Freshers to Intermediate to Candidates From Higher Experience Brackets –

  1. What is “Data Wrangling” in Data Analysis?

It is a process of eradicating errors and merging data sets to make them accessible and easier to analyze

  1. What is Data Validation?

It is a process of checking and ensuring the quality and accuracy of data

  1. Explain the best Data-cleaning technique

Remove unnecessary values, duplicate data, and typos, alter data types, search for missing values, use clear format, interpret language, remove unwanted exceptions.

  1. What is time series analysis?

The precise way of analyzing data collected at consistent intervals rather than intermittently or randomly.

  1. What are the properties of clustering algorithms?

Hierarchical or flat. Hierarchical algorithms induce a hierarchy, decreasing generalization, flat algorithms, all clusters are the same

  1. Explain the involvement of collaborative filtering

Collaborative filtering is a technique used to filter out items based on the conduct of similar users. Eg. Amazon suggests items or products which match the choices as per previous purchases.

  1. Explain Univariate, Bivariate, and Multivariate Analysis

The purpose is to study the relationship among the variables. Univariate analysis studies one variable, bivariate studies two variables and the relationship between them, and multivariate studies more than two and the connection between them.

 

  1. What is Metadata?

In simple language, it means data about data or information about various aspects of the data.

  1. List top Python Libraries

TensorFlow, NumPy, SciPy, Pandas, Matplotlib, Keras, SciKit-Learn, PyTorch, Scrapy, BeautifulSoup, LightGBM, ELI5, Theano, NuPIC, Ramp, Pipenv, Bob, PyBrain, Caffe2, Chainer

  1. Name some frequently applied statistical techniques

Two main statistical techniques are mainly used by data analysts: descriptive statistics, which summarizes data using indexes, and other is inferential statistics, which concludes from data.

Some of the bigwigs who recruit candidates with Top Data Analyst Skills are Google, Amazon, Facebook, Microsoft, IBM, Apple, Uber, Netflix, Airbnb, and LinkedIn.

 

FAQs: 

Below are the FAQs about the data analyst skills.

Which are the data analysis tools?

A few data analysis tools that can be learned are R, Python, Tableau, SAS, Apache Hadoop, and MS Excel.

What are the different kinds of jobs in the Data Analysis field?

Data Analyst, Data Scientist, Data Engineer, Data Analytics Consultant, Operations Analyst, Marketing Analyst, IT Systems Analyst, etc.

How much do Data Analysts typically earn per annum?

The average salary of a candidate acquiring Top Data Analyst Skills for fresher to 10+ years of experience is approximately 5 lacs to 20 lacs.

What is better, SQL or Python?

The performance in SQL is better and faster than in Python.

What is a PAC model?

PAC model means Plan, Analyze, and conclude is a framework used to conduct a thorough analysis.

Conclusion –

Till the point technology continues to evolve, the data analysis profession will have no dearth of opportunities. Automated solutions, increase the demand for data analysts. To have a great and rewarding career in Data Analysis, acquiring and honing these Top Data Analyst Skills, will be a boon for the aspirants in the job market. So, are you ready to take a step forward in this ever-growing field? Start with learning data analyst skills.

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