Data Analysis guide

Data Analysis guide

Introduction

Finding patterns is what the human brain does. We are wired to analyze and find patterns in anything, from the weather to the mood of our partners, we try to predict it all.

Imagine a domain of work where people get paid handsomely just for that. Data Analysis is no new domain and has been practiced by mankind throughout our history.

Analyzing data to make informed decisions is something we do every day. With the advancements in computers, now it is possible to do the same with large data sets.

Data analysis deals with analyzing these big data sets to make informed decisions to help improve efficiency in anything and everything.

What is Data Analysis?

Data analysis refers to the use of data to guide decision-making in a Business. This is achieved by sorting, structuring, and converting data to derive useful information with relevance to business functions.

This is something that we adopt on a day-to-day basis while making decisions. For example, when we make purchases, we tend to research to find the best price, quality, and standard of a certain product before making the purchase.

When the same premise is followed with the use of big data in a business decision, it becomes data analysis.

What is Data Analysis Process?

The data analysis process refers to the mechanism deployed to make use of data to inform business decisions. It breaks down the various processes data needs to go through before it can help guide business decisions.

You can learn more about the data process from our blog on Data Processing. Data analysis processing is done over 4 basic stages. The data process is carried out at each stage to help answer and relevant business questions.

The first step, even before we begin the data analysis process. Identification of business questions is a good place to start. Knowledge of questions the business is trying to answer can help guide the data process.

Data Analysis Process

1) Data Collection –

Raw data sets relevant to the business questions have to be collected. They might be from internal sources like company logs, and external sources like government records.

2) Data Cleaning –

Big data sets contain a lot of irrelevant or moot data. The data needs to be cleaned by removal of duplicates, repeated data, inconsistent data, and data anomalies. The remaining data needs to be structured properly.

3) Analysis of data –

Data analysis techniques are employed to find patterns, exceptions, variations, and proportional relations, within data sets. Data analysis software might be used to help aid in data visualization.

4) Data interpretation –

The results borne out of analyzing the data are interpreted to best answer the original business questions.

What is Data Analysis in Research?

Analyzing data in research is not much different from analyzing data for business decisions. Here the objective is to study the data itself and not answer any pre-conceived questions.

Our mind is trained to observe and spot patterns. Naturally, we do the same in data analysis. Sometimes, when having a question beforehand, we give in to our biases and interpret data in a way that benefits our story.

Analyzing data in research needs to be devoid of such biases. Data analysis in research can unearth and reveal exciting and unexpected data. This can be accessed only by keeping an open mind while analyzing data in research.

Data is classified on its nature into three Data types to help in analyzing data in research.

Quantitative data –

Data that is represented using numerical values. This data represents values that can be calculated.

Qualitative data –

Data is represented with descriptions or words. This data can be observed and has value. However, it is subjective and therefore cannot be used in calculations.

Categorical data –

Data that is available and presented in groups. It is important to note that data so grouped cannot be repeated in another grouping.

What are the types of Data Analysis?

Even though the underlying data analysis process is the same, they are segregated into various types of Data Analysis based on the function served.

Data Analysis Types

There are four major types of Data Analysis. These types of Data analyses are classified based on the kind of business question they help us answer.

 

1) Descriptive analysis

These types of data analysis help us answer the question of “what” happened concerning certain events. This is done by the use of quantitative data to present statistics. These statistics help identify any event.

For example, sales data can help us understand things like money spent per sale, time per sale, the peak time for sales, etc.

2) Diagnostic analysis

As the name suggests, it helps diagnose any occurrence. This helps us understand “why” something happened.

For example, if the descriptive analysis showed an increase in sales, the diagnostic analysis can help us understand the reason for this increase.

3) Predictive analysis

As the name suggests, these types of data analysis help to make an informed prediction of future events. They try to answer “What might happen” with regards to the future of any process.

For example, if the descriptive analysis indicates an increase in sales and the diagnostic analysis indicates an increase due to a lockdown during a certain month, the predictive analysis can help predict what might happen in a future lockdown.

4) Prescriptive analysis

These types of data analysis aim to provide a recommendation of action regarding any event. This answers the question of “What must be done” regarding a given event.

The information gathered from the first three types of data analysis is used to make recommendations on how the business should act.

Continuing with the example of sales, the company might decide to stock up on products, or increase marketing, in the case of another lockdown.

Why is Data Analysis important?

Data analysis techniques and tools developed over the past half-century have made the data analysis process easier and possible for large data sets. Performing data analysis helps businesses make informed decisions rather than a shot in the dark.

This helps increase efficiency within a business, leading to fewer bad decisions, subsequently resulting in higher revenue for the company.

Some benefits of performing data analysis

- Identifying clients: Helps understand client demographics to better target future clients.

- Efficient targeting: Data analysis identifies the most effective channels of marketing for reach.

- Innovation: Data analysis can help guide future innovations.

- Reduces operation costs: Helps identify areas for a cut to optimize budget spending.

- Problem-solving: Problems arising in business halt progress. Inefficient addressing of problems can further affect the project. Data analysis helps make informed decisions on problem-solving.

Big Data Hadoop

A career in Data Analysis

Data analytics is fast becoming one of the most sought-after domains for a lucrative career. This is due to the wide range of applications for data analytics as it can help businesses in every field.

This has led to a sharp rise in demand for data analysts across the globe. In fact, according to the U.S. Bureau of Labor Statistics, the demand for data science skills will lead to a 27% rise in employment by 2026. 

There are positions available even for beginners in the form of a junior analyst. As you scale up using certifications, you can level up to more lucrative positions.

Learn how to become a successful Data Analyst with a lucrative job profile from our blog titled “How to become a Data Analyst”.

Once you gain experience in data science you can upscale with certificates like CompTIA Data+ and Big Data Hadoop to advance into higher job profiles.

Some of the profiles you can get into –

Data Scientist –

Becoming a data analyst is an ideal starting point for becoming a data scientist. You can do a Data Science Masters Program from a reputed and authorized training partner like Sprintzeal.

The course will help you understand machine learning and advanced mathematics apart from advancing your programming skills to help you become a successful Data Scientist.

Data Specialist –

You can choose to specialize within a niche of data or within a particular type of industry to become a specialist. For example, a financial analyst is a specialist data analyst within the finance niche.

Apart from gaining a thorough knowledge of the niche, upgrading through certifications like Big Data Hadoop can be highly beneficial.

Data Consultant –

After gaining enough experience and upskilling through certificates, you can become a freelance consultant. This opens you up to a wide range of work and offers flexibility in work style.

One needs to gain industry experience apart from doing certifications like CompTIA Data+ to instill belief among clients regarding your ability.

Data Management –

A lot of experienced data analysts move into management positions. Experience in informed decision-making is a quality useful in any management role.

Within Data analytics you can take up roles like data analytics manager, director of data analytics, and in some companies, chief data officer.

In case you are headed out to an interview for a job as a Data Analyst, be sure to check out our blog on Data Analyst Interview Questions and Answers.

The average base pay for some roles in Data Analytics in the United States, according to Glassdoor

Role

Average Base Pay

Data Analyst

$69,517 /yr

Senior Data Analyst

$96,809 /yr

Data Scientist

$1,17,212 /yr

Senior Data Scientist

$1,42,258 /yr

Lead Data Scientist

$1,38,010 /yr

Data Analyst IV

$1,05,388 /yr

Start your lucrative career in Data Analysis with Sprintzeal today!

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Akssar

Akssar

A law graduate with an immense passion for research and writing. Loves to travel, read and eat. When not doing that, loves working toward bringing well-researched and informative content to readers. Has experience in, and, is passionate about journalistic pieces, blog posts, review articles, sports coverage, technical research pieces, script-writing, website content, social media marketing, advertising, and creative writing. Sleeps when the ink runs out writing all that.

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