How‌ ‌to‌ ‌Become‌ ‌Data‌ ‌Analyst‌ ‌

A data analyst is responsible to gathers, process, and conduct statistical analyses on big data. He figures out how knowledge could be used in answering the queries and solving the problems. With the evolution of technology, data processing has also improved. The introduction of the relational DB provided data analysts a fresh breath, allowing them to extract data from DBs using SQL.

First ask yourself, which field are you interested in? Is it data science or big data or data analysis?

If you’re interested in pursuing a data analytics career, the following are the five points to follow:

1. Hold a bachelor’s degree in a discipline with a concentration on mathematical and analytical abilities, such as computer science or mathematics.
2. Enroll in a data analysis Bootcamp program.
3. Gain major skills in data analytics.
4. Try getting data analytics certifications.
5. Get an entry-level data analysis job.
6. Acquire a master’s degree in the field.

How‌ ‌to‌ ‌Become‌ ‌Data‌ ‌Analyst‌ | Data Analyst Job Description

Many data analytics jobs include the processing and cleaning of information to discover patterns and market insights. Based on the product or sector or the data analytics type that you deem your expertise, the daily data analyst job varies. It may be the responsibility of data analysts to build dashboards, develop and manage DBs and applications for various departments within their company using BI software, coding, and Tableau.

In order to evaluate organizational priorities, most data analysts collaborate with IT teams, managers, and data scientists. They extract and clean data from quantitative and qualitative methods and then use common statistical tools and techniques to evaluate and assess findings. In certain cases, in large data sets, they detect trends, associations and similarities and discover potential process improvement strategies. Data analysts also work on creating reports based on their research and plan and propose further project phases to all the involved stakeholders.

Data Analyst Salary

Salary figures are contingent on work assignments. A professional data analyst will demand a high price with the expertise of a data scientist whereas a beginner will settle for a little less.

Salaries for Data Analysts:

According to Payscale and Glassdoor, the average salary for beginner-level data analysis jobs is around $83K-$100K/annum. The average salary for professional-level data analysis is $118K-$142K/annum.

Data Analyst Job Outlook

The data analysts of today should be ready for a transition. The tasks of analysts are now becoming increasingly challenging. In order to produce valuable observations and decisions, professional analysts use modelling and predictive analytics strategies. Then they have to demonstrate to rooms of puzzled individuals what they have found. They have to turn themselves from data analysts into data scientists, in easier words.

As per latest information from the Bureau of Labor Statistics, the positions for beginner-level data analysis are projected to increase by 20 per cent and expert-level data analyst positions are all anticipated to expand by 14 per cent, which is much higher than the overall economic growth. Since data analysts can fit into most sectors, technological advancement contributes to the growth of even more analyst jobs. At each and every step, we gather data, its organization, and the presence of predictive analysis allows society to become a better professional.

In-demand Data Analysis Skills

Following are the most in-demand data analysis skills:

Critical Thinking: 

Inquisitiveness and innovation are the main characteristics of a successful data analyst. To think about challenges with a critical and technical lens is necessary to build a strong foundation in statistical analyses, but perhaps more crucial. This will assist the analyst in producing insightful research topics that will strengthen the knowledge of the subject at hand by a business.

Programming Languages: 

Data analysts must be fluent in any programming language and must-have the know-how of others. For data processing, statistical analysis, data cleaning, and data visualization, data analysts use programming languages like SAS, Python, and R.

Data Visualization: 

Efficient representation of information takes time and practice. Based on their audience, a good data analyst recognizes what kinds of charts to use, how and when to scale visual representations and knows which graphs to use.

Good Communication Skills: 

Data analysts need to express their observations distinctly, be it to a consumer, reader, or a small group of managers making monetization strategies. The secret to success is good communication.

Data Warehousing: 

A few analysts work on the back-end as well. To build a data warehouse, they link repositories from various sources while using query languages to search and process data.

Machine Learning: 

Individuals with machine learning abilities are highly useful, while traditional data analyst positions are not supposed to have machine learning expertise.

Database Querying Languages: 

Data analysts are also well-versed with the popular querying language such as T-SQL, PostreSQL, and PL/SQL.

Data Mining and Cleaning: 

When data isn’t neatly stored in a database, data analysts must use other tools to gather unstructured data. Once they have enough data, they clean and process through programming.

SQL Databases: 

A good data analyst is familiar with SQL databases too. Relational DBs of structured data are SQL databases. Data is kept in tables to conduct analysis; a data analyst extracts data from diverse tables.

Advanced MS Excel: 

Last but not the least, data analysts must have a good know-how of MS excel and recognize advanced analytics and modeling processes.

Popular Data Analysis Tools

Below is the list of some of the popular tools utilized by Data Analysts:

Tableau: 

Researchers use Tableau for data aggregation and analysis. They will read and distribute analytics and create visualizations with various team members.

Jupyter Notebook system: 

This makes checking code easy for data analysts. Due to its markdown feature, non-technical guys love the simple structure of this tool. 

Google Analytics: 

GA lets analysts understand customer information, particularly trends and market service areas which need to be enhanced on landing pages or calls – to – actions.

AWS S3:

It is a management framework for the cloud. It can be used by data scientists to store and recover massive datasets.

Github:

 It is a forum for technological project sharing and creation. A must have for data analysts who are familiar with OOP coding.

Data Analyst Role & Responsibilities

For a data analyst, the everyday routine relies on their job role and what resources they practice. Many data analysts choose statistical tools and MS Excel without using coding. Some analysts conduct regression analysis or develop data visualizations, based on the issues they are attempting to solve. Senior data analysts are often labeled as beginner-level data scientists. In certain examples, in the morning, a data analyst/scientist might write queries or answer standardized requests and create custom approaches. Then in the evening, the same individual will experiment with database systems such as NoSQL and Hadoop.