LEARNING DATA ANALYTICS AS A BEGINNER
Tags: datascience, beginners
Published: 2023-04-09 23:43:25

### ***Introduction***

In this time and age, where Technology keeps growing and getting better by the day, the use of big data keeps growing globally. Businesses now understand the value of using data to grow their businesses better than ever. I know you’re wondering ***HOW*** and ***WHY***.


Well I’m here to enlighten you…This essay is to help guide anyone interested in breaking into technology as a data analyst or transitioning from any aspect of technology to data analytics. This quick read will take you from knowing nothing about data analysis to being able to explain it to others. If you want to know more, keep reading.


### ***Prerequisites for Learning***

1. You need a laptop or computer for better learning.
    
2. You need mobile data or WIFI (good internet connection).
    
3. Zeal and passion to learn.
    

**By the end of this short read, you will:**

* Understand the fundamentals of being a data analyst.
    
* Familiar with the process and techniques of analyzing data.
    
* Understand the benefit of being a data analyst.
    

### ***What is Data Analytics?***

Data analytics involves the gathering of raw data, the preparation and subsequent analysis of that data, the sharing and drawing of valuable insights from that data, and its interpretation (using it to explain certain scenarios and outcomes in order to make informed decisions through storytelling).

Data analysts use data from things in the past and use it to predict what is likely to occur. As a data analyst, you can make sense of the past and predict future trends and behaviors.

Data analysis, when done well, allows for faster and better business decisions to increase sales and purchase behavior.


### ***Data Analytics Process***

Here are the five most important processes for analyzing data:

1\. Define the Question

2\. Data Collection

3\. Data Cleaning

4\. Analyze the data

5\. Create visual representations

1\. **Defining the question** allows you to know what problem you want to solve. A data analyst's job is to understand the goals and problems that must be solved, which leads to the next step.

2\. **Collecting the data** needed because it is only once you recognize what is essential that you will understand what data is required to solve the problem.

3\. **Cleaning the information** is important because failure to do so disrupts the entire process. Incorrect or repeated data equals false data; when this occurs, the analysis is incorrect. Cleaning the data is necessary to ensure that you are working with high-quality data.

4\. **Analyzing data** involves using various techniques and tools to find trends and variations that tell a story important to solving the "problem."

5\. **Creating visual representations** is also known as data visualization. It is a way of representing information graphically, highlighting trends and patterns in data. A data visualization dashboard helps anyone who looks at your analyzed data gain a quick understanding.

Now, you have completed your analysis, you have your data visualization dashboard, and you have your ideas. You can now share your findings (insights) with individuals or key stakeholders in your organization. With your analysis, key stakeholders will be able to make more informed decisions that will help the organization move forward, reduce costs, or increase profit.

The sole aim of data analysis is to develop solutions and predict future trends based on the analyzed data.


### ***Types of Data Analytics***

**1\. Descriptive Analytics**

Descriptive analytics helps you summarize or describe the characteristics of your dataset in a meaningful way. It is a basic level of analytics where you build your dashboard with the information you have acquired. This is the stage where you build reports and make a presentation with the authentic data gathered.

**2\. Predictive Analytics**

Predictive analysis answers the question, **“What is most likely to happen?”**

Predictive analytics helps you estimate what will happen in the future or what will happen as a result of a certain decision. It predicts a trend.

By using patterns found in older data as well as current events, predictive analytics can predict future events.

**3\. Diagnostic Analytics**

Diagnostic analysis answers the question, **“Why did this happen?”**

Diagnostic analytics can be considered as a form of exploration and an early evaluation of your dataset, where you seek to understand and summarize its main characteristics. Thus, the diagnosis of data helps you understand how your data is structured, spot potential patterns and trends, and find any anomalies. It is crucial for figuring out whether the methods of analysis you intend to apply later are appropriate for your dataset.

**4\. Prescriptive Analytics**

The prescriptive analysis answers the question, **“What should I do?"** It is when the data itself recommends what should be done. Prescriptive analytics entails combining all the knowledge discovered through other methods of data analysis and using it to identify the most effective strategy.

It is a powerful sort of analytics because you have to go through the diagnostic, predictive, and descriptive approaches in order to arrive at the prescriptive analysis. This strategy is essential to businesses.

It is significant to remember that occasionally, just one sort of data analytics can be used to solve a problem.


### ***Tools Used by Data Analysts***

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/s51nhka0ajtbprbbr5rs.jpeg)
Data analysts utilize a variety of tools and skills. I'll be analyzing a few important ones:

**1\. Microsoft Excel**

It is a spreadsheet software. It is used for data analysis calculations and graphing operations. It is a useful tool for data analysis, especially for beginners. Unfortunately, it is rather slow when dealing with large datasets.

**2\. Microsoft Power BI**

It is a business analytics tool for data visualization. It allows users to create interactive visual reports and dashboards, and with a minimal learning curve, you will soon be a pro at using Power BI to graphically present data.

**3**. **Tableau**

Tableau is a data visual analytics platform that makes it easier for people to explore and manage data and faster to discover and share insights that can help businesses make informed decisions. It allows people and organizations to make the most of their data.

**4\. Python**

Python is a popular general-purpose programming language that can be used for a wide variety of applications. It’s a free, open-source scripting language that is designed to be used for machine learning, building websites, software testing, data science, and data analytics. It is utilized in practically every important aspect of technology. When it comes to data analytics, Python is used for everything from data gathering to analysis and reporting.

**5\. Jupyter Notebook**

It is used by data analysts in presenting data. It allows you to create interactive documents. It allows you to combine live code, equations, visualizations, and interactive text. Some people use Google Slides to present data, but Jupyter Notebook is far more interactive and specifically designed for data analytics.


### ***Benefits of Learning Data Analytics***

I will be concluding this eye-opening read with the astonishing advantages of learning ***data analytics.***

**1\. Data Analysts are in high demand**

Skilled experts in data analysis are one of the most sought-after professionals in the world. This is because data analysts are in high demand. It is an excellent career that requires dedication. A data analyst is required in any organization that uses data. A large number of organizations collect and use data. It is unavoidable that companies will need experts to process their raw data and produce meaningful information and insights that can help move their agenda forward, whether it is to reduce cost or increase profit.

According to an exclusive report by MarketsandMarkets, the big data market is growing rapidly and exponentially, and it’s estimated that, by 2025, it will be worth a whopping sum of 229.4 billion US dollars. That is huge, so you should hop in and get your share!

**2**. **It is highly profitable**

As a result of the high demand for data analysts, It makes data analysis a well-paying career. A data analyst position commands a high salary and wonderful perks from entry-level to senior-level positions.

![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/juebiki9lozs8jpjwgmq.jpeg)
In the United States of America, junior and senior data analysts get competitive salaries that range from $57,000 to $74,000 a year. The attractive financial compensation in salaries and perks makes data analytics a good career to consider.

**3\. Anticipated Job Growth**

Opportunities for skilled data analysts are numerous across different industries. Companies have adopted big data, and they need data analysts to work in this field. Data analysts can look for work in telecommunications, technology, advertising, and insurance. Other sectors include health, finance, research, and development. The opportunities are numerous for those who want to pursue a career in data analytics.

**4\. It is highly valued**

It is a highly valued niche in the technology space. Data analysts are in high demand as data generation and collection increases. Businesses need to increase value in their business, so they sought out data analysts so they could use real-life insights to make better, more informed decisions. Demand equals value.

According to an IBM analysis, data analysts will continue to be highly appreciated across industries.

**5\. Contribute to the decision-making process**

A career in data analysis is both challenging and stimulating. It involves solving complex problems and making a real difference. This type of work is highly fulfilling, and being among the people in this space is worthwhile. You can help businesses maximize value by making more informed decisions and developing products that the world (customers) requires, by gathering raw data, analyzing it, and interpreting valuable insights.