Data Analyst: Career Path, Skills, Qualifications and Responsibilities

26Feb,2020
By Namrata Padhi

Data-analysis is one of the most demanding career path right now as we are in a technology-driven era that helps to handle the growing needs of many companies and consumers emerging every day. Data analyst jobs can be found throughout a diverse mix of companies and industries. Any company that uses data needs data analysts to analyze it. Some of the highest paying jobs in data analysis involve using data to make investment decisions, target customers, assess risks or decide on capital allocations. In this article, we have given a complete guide to become a Data Analyst.

 

 

Meaning of Data Analysis:

Data analysis is a process of collecting and performing statistical analyses on a large data-set. Many companies require processing of huge data and this job is done by experts know as Data Analysts. They discover how data can be used to answer questions and solve problems. The data analyst jobs vary depending on the industry or the type of data analytics you are interested in. They help in creating dashboards, designing, and maintaining relationship databases and systems for different departments throughout their organization using business intelligence and programming.Skilled data analysts are some of the most sought-after professionals in the world.  

 

What do Data Analysts do ?

Data Analysts help other people make the right decisions and prioritize the raw data that has been collected to make work easier using specific formulas and applying the right algorithms.

Data analysts take mountains of data and probe it to spot trends, make forecasts, and extract information to help their employers make better-informed business decisions. The career path you take as a data analyst depends in large part on your employer. Data analysts work on Wall Street at big investment banks, hedge funds, and private equity firms.

Many companies also label data analysts as information scientists. This classification typically involves working with a company’s proprietary database. Many information scientists work with core database infrastructures thus also gaining skills in other applicable technical areas such as data infrastructure building and development. Technology companies are unique because as technology changes rapidly, the dynamic of the company often changes too. Departments are constantly being created to tackle new challenges and pursue new market opportunities.

 

How to become a Data Analyst ?

Qualifications needed to become a Data Analyst are : 

1-First, you have to earn a bachelor’s degree in the field with knowledge in statistical and analytical parts, such as mathematics, computer science, or statistics.

2-You have to learn important data analytics skills

3-Consider certification

4-Get your first entry-level data analyst job

5-Earn a master’s degree in data analytics

 

Skills required to become a Data Analyst:

Programming Languages (R/SAS)- Data analysts use programming languages such as R and SAS for data gathering, data cleaning, statistical analysis, and data visualization.

Creative and Analytical Thinking- curiosity and creativity are the key elements of a good data analyst. It is very important to have good knowledge of statistical methods but even more critical to think through problems with a creative and analytical lens

Strong and Effective Communication: strong communication is the key to success, and there is no doubt about it. So, a data analyst must clearly convey their findings

Data Visualization: A good data analyst understands what types of graphs to use, how to scale visualizations, and know which charts to use depending on their viewer.

Data Warehousing: Some data analysts work on the back-end to connect databases from multiple sources to create a data warehouse and use querying languages to find and manage data.

SQL Databases-Relational databases with structured data called SQL databases. Data is stored in tables, and a data analyst pulls information from different tables to perform analysis.

Database Querying Languages- The most common querying language data analysts use is SQL, and many variations of this language exist, including PostgreSQL, T-SQL, PL/SQL.

Data Mining, Cleaning, and Munging-  Data analysts must use other tools to gather unstructured data in case the data isn’t neatly stored in a database.

Advanced Microsoft Excel- Data analysts must know how to use excel properly, and they should understand advanced modeling and analytics techniques.

 
Future prospects of Data Analysts may include:
 
According to recent data from the Bureau of Labor Statistics, market research analyst positions are expected to grow by 20%, and management analyst positions are all expected to grow by 14%, which is much faster than the average job growth.The various prospects are :
 
1- Working with IT teams, management and/or data scientists to determine organizational goals
 
2- Extracting data from primary and secondary sources
 
3- Clean and trim data to discard irrelevant information
 
4- Analyzing and interpreting results using standard statistical tools and techniques
 
5- Pinpointing the trends, looking after correlations and patterns in complicated data sets
 
6- Identify new opportunities for process improvement
 
7- Providing concise data reports and clear data visualizations for management
 
8- Designing, creating and maintaining relational databases and data systems
 
9- Emphasize code problems and data-related issues
 
10- Database design
 
11- Data mining
 
12- Data cleaning and munging
 
13- Data visualization and reporting techniques

 

 

Top 10 Data Analytics Courses:

 
1.Data Analytics for Lean Six Sigma
 
Topics:- 
i) Data and DMAIC
ii) Descriptive statistics
iii) Hypothesis testing and Causality
iv) Introduction to ANOVA
v) Introduction to data analytics for lean six sigma
vi) Introduction to lean six sigma
vii) Introduction to Minitab: installing and loading data
viii) Kruskal-Wallis test
ix) Normal, lognormal, and Weibull distribution
x) Organizing data
xi) Pareto analysis
xii) Population vs. sampling
xiii) Probability plot and empirical CDF
xiv) Selecting CTQs
xv) Visualizing numerical and categorical data
 
2.Introduction to Data Analytics for Business:
 
Topics:- 
i)Analytical organizations: roles and structures
ii) Aggregating and sorting data in SQL
iii) Big data & the cloud
iv) Conceptual business models
v) Data analytics tools
vi) Data captured by source systems
vii) Data extraction using SQL
viii) Data governance, privacy, and quality
ix) Data storage and databases
x) Extending SQL queries using operators
xi) Introduction to SQL
xii) The Information-Action Value Chain
xiii) The relational database
xiv) Virtualization, Federation, and In-Memory Computing
 
3.Beginner Statistics for Data Analytics –
 
Topics:- 
i) Coefficient of variation
ii) Correlation and causation
iii) Creating and understanding a regression
iv) Fundamentals of statistics
v) Inferential statistics: probability distribution, normal distribution, Central Limit theorem, estimates, and confidence interval estimate
vi) Introduction to regression analysis
vii) Introduction to standard deviation and variance
viii) Mean, median, and mode
ix) Understanding and creating histograms
 
4.Python:
 
Topics:- 
i) A crash course in Python
ii) Data cleaning
iii) Data grouping
iv) Data visualization with Pandas
v) Import and export data from Pandas
vi) Installing and setting up Python
vii) Introduction to data analysis
viii) Introduction to the data frame
ix) Introduction to Pandas and NumPy
x) Introduction to series
xi) Working with text data
 
5.Beginner’s Guide to Data & Data Analytics:
 
Topics:- 
i) Classification of data analytics tools
ii) Data pipelines
iii) Data types, files, and formats
iv) Introduction to data
v) Key data analytics concepts and terminology
vi) Roles and skills of data professionals
vii) The data analytics “Tool Triangle.”
 
Read, Top 12 Most Preferred Data Science Careers
6.Advanced Business Analytics Specialization:
 
Topics:-  
i) Introduction to Data Analytics for Business (Course 1)
ii) Predictive Modeling and Analytics (Course 2)
iii) Business Analytics for Decision Making (Course 3)
iv) Communicating Business Analytics Results (Course 4)
v) Advanced Business Analytics Capstone (Course 5)
 
7.R Level 1 - Data Analytics with R:
 
Topics:-
i) Creating objects
ii) Data types
iii) Functions in R
iv) Graphs in R
v) Introduction to the R programming language
vi) Looping
vii) Using the R Commander GUI
viii) Working with strings
 
8.Health Information Literacy for Data Analytics Specialization:
 
Topics:- 
i) Healthcare Data Literacy (Course 1)
ii) Healthcare Data Models (Course 2)
iii) Healthcare Data Quality and Governance (Course 3)
iv) Analytical Solutions to Common Healthcare Problems (Course 4)
 
9.Data Analytics: SQL for newbs, beginners, and marketers:
 
Topics:- 
i) Aggregating, grouping, and sorting
ii) Basic SQLcomands
iii) Basics of SQL
iv) Importing data on Windows
v) Increasing speed using indexes
vi) Installing SQLite on Linux, macOS, and Windows platforms
vii) Joining and merging tables
viii) Overview of SQL databases
ix) Spark SQL
 
10.Social Media Data Analytics:
 
Topics:- 
i) Analyzing social media data using Python
ii) Analyzing social media data using R
iii) Analyzing structured data
iv) Data visualization
v) Introduction to Python programming
vi) Introduction to R
vii) Python for Data Analysis, Econometrics, and Statistics
viii) Structured vs. unstructured data
ix) Twitter libraries
x) Using Python for extracting data from Twitter and YouTube
 
Read Here, Analyzing the Difference between Data Science vs. Big Data vs. Data Analytics

 

Jobs In Data Analytics Sector:

Jobs in the data analytics sector are plentiful, salaries are high, and the career paths you can take are abundant. Data analytics offers a wide variety of opportunities across industries and corporate levels. As such it can be difficult to pinpoint salary and growth expectations. See the two of the most sought after job mentioned below:

Financial Analyst

The financial analyst category is generally the most widely encompassing classification for data analysts. This type of role can include business analysts, management analysts, and a wide variety of different types of investment analysts.

Salary: In India, a person can earn up to 10 lakhs per annum as a financial analyst however in U.S BLS data shows the average hourly wage for a financial analyst at $48.55 with an average annual salary of $100,990. Hourly salaries can range from $25 to $80. Financial analysts in New York make the most at an average hourly wage of $66.

Market Research:

A second Bureau of Labor classification often looked to for the salary expectations of data analysts is the market research analyst category.

Salary: This category shows the average hourly wage at $34.11 with an annual salary expectation of $70,960. Hourly wages for market researchers can range from $16.50 to $58.21

 

Career Paths Of Data Analyst:

Below is a list of some of the many different roles that you may encounter when searching for or considering data analysis.
 
Corporate strategy analyst:  this type of role will focus on analyzing company wide data and advising management on strategy direction. This role may also be focused on mergers and acquisitions.
 
Business analyst: Analyzes business specific data.
 
Management reporting: Reports data analytics to management on business functions.
 
Budget analyst: Focuses on the analysis and reporting of a specified budget.
 
Compensation and benefits analyst: Usually part of a human resources department that analyzes employee compensation and benefits data.
 
Insurance underwriting analyst: Analyzes individual, company, and industry data for decisions on insurance plans.
 
Web analytics: Analyzes a dashboard of analytics around a specific page, topic focus, or website comprehensively.
 
Sales analytics: Focuses on sales data that helps to support, improve, or optimize the sales process.
 
Actuary: Analyzes mortality, accident, sickness, disability, and retirement rates to create probability tables, risk forecasting, and liability planning for insurance companies.
 
Fraud analytics: Monitors and analyzes fraud data.
 
Business product analyst: Focuses on analyzing the attributes and characteristics of a product as well as responsibility for advising management on the optimal pricing of a product based on market factors.
 
Credit analytics: The credit market offers a wide need for analytics and information science in the areas of credit reporting, credit monitoring, lending risk, lending approvals, and lending analysis.
 
Social media data analyst: Social media and growing tech companies rely on data to build, monitor, and advance the technology and offerings that social media platforms rely on.
 

Machine learning analyst: Machine learning is a developing technology that involves programming and feeding machines to make cognitive decisions. Machine learning analysts may work on a variety of aspects including data preparation, data feeds, analysis of results, and more.

Read, Short-term Courses: Benefits, Precautions, Best courses

 

Responsibilities of a Data Analyst :

The day to day responsibilities in the life of a Data Analyst includes :

1. Designing and maintaining data systems and databases; this includes fixing coding errors and other data-related problems.
2. Demonstrating the significance of their work in the context of local, national, and global trends that impact both their organization and industry.
3. Using statistical tools to interpret data sets, paying particular attention to trends and patterns that could be valuable for diagnostic and predictive
analytics efforts.
4. Mining data from primary and secondary sources, then reorganizing said data in a format that can be easily read by either human or machine.
5. Preparing reports for executive leadership that effectively communicate trends, patterns, and predictions using relevant data.
6. Collaborating with programmers, engineers, and organizational leaders to identify opportunities for process improvements, recommend system modifications, and develop policies for data governance.
7. Creating appropriate documentation that allows stakeholders to understand the steps of the data analysis process and duplicate or replicate the analysis if necessary.

 

 
Data Analysis Tools:
There are many tools used by the Data Analyst to extract information and perform their duties. Some of the most popular tools are :-
1. Microsoft Excel®
2. SQL
3. SAS® software
4. Google Analytics ™
5. Google Tag Manager
6. Tableau™
7. Google AdWords™
 
Data Analyst Salary :
Salary of a data analyst, depends on job responsibilities and industry sector. A senior data analyst gets a package of $118,750-$142,500, whereas the average salary for entry-level data analysts is $83,750. In India, the salary offered varies from 3 L.pa to 4 L.pa  for freshers according to the skill requirements while with 1- 4 years experience average salary is 5 L.pa and for more than 4 years experience starts from 6 L.pa.
 
Data Analytics Salary of the Top Companies in India
 
Accenture’s Data Analytics Salary in India: 90% gets a salary of about Rs 980,000 per year
Tata Consultancy Services Limited Data Analytics Salary in India: 90% of the employees get a salary of about Rs 550,000 per year. A bonus of Rs 20,000 is paid to the employees.
EY (Ernst & Young) Data Analytics Salary in India: 75% of the employees get a salary of Rs 620,000 and 90% of the employees get a salary of Rs 770,000.
HCL Technologies Ltd. Data Analytics Salary in India: 90% of the people are paid Rs 940,000 per year approximately.

 

Conclusion:

Being a Data analyst involves understanding various aspects such as finance, administration, and management. It is one of the most lucrative professions change as the organizational decisions are completely based on the data acquired. Data Analyst certifications are the most sought after certifications for entry-level as well as experienced professionals. There are many renowned institutes like Yoda learning offering certifications for various tools that are used for business analytics. Be a Data Analyst and take your career to the next level
 
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