Chapters

Data? What does it entail? Data is defined based on information about a specific subject, which is collected and analyzed to solve problems. Data is associated with data visualization such as graphics, histograms, and charts which is essential in understanding and analyzing data.

**Data science involves recording, analyzing, and storing data to get future predictions from data.** It is used to mine large sets of structured and unstructured data to detect, identify patterns, and gain meaningful insight.

Data science uses mathematical and computer science to sort out and analyze big data sets. Also, data scientists use data science to manage datasets and get new valuable insights. These insights are used for drawing predictions and solving real-life problems.

Since the world and Nigeria inclusive is evolving and almost everything is digital, mathematical science has been transformed into data science. Data science uses machine learning and programming languages such as python to analyse data.

**When learning data science, you’ll be introduced to different tools used in analysing data.**

These tools aid you through the process of sorting big data sets, analyzing them, creating great visualization for the data sets, and building predictive models using machine learning algorithms and programming software.

Some of these tools are listed below:

- Python
- Stata
- Klime
- SAS
- Apache Hadoop
- TensorFlow
- Excel
- MATLAB
- Tableau and more!

## Learning Basic Statistics Methods and Concepts

**Data science seems to play an essential role in almost every sector in Nigeria and the world at large**. This is where the knowledge of basic statistical methods and concepts comes in. Statistics is used in research for professional fields such as business, medicine, finance, education, etc.

The machine learning, programming language, and algorithms used in data science require basic statistical concepts to run them. Data science uses mathematical and computer science to analyze a large amount of data.

This means to learn data science, you must know statistics, programming language, and machine learning.

**Data science for beginners involves knowing all the basic statistical and mathematical terminologies and calculations.**

While data analytics uses mathematical knowledge to arrange and model data, data science focuses on extracting information and drawing new predictions from the data models.

There are basic skills you need to have before learning data science, and they include:

- Probability theory
- Mathematics
- Prediction
- The measure of central tendency (mean, median, mode)
- Computer science
- Calculus
- Algebra
- Regression

To get started with learning or re-learning these basic skills, you can check online for e-books and resources. **You can also check out websites like Superprof for tutors who can offer online or face-to-face lessons.** Doing this will enable you to get better at statistics and also data science.

## Data Analysis Methods

You can’t talk about data science without talking about large data amounts. Analyzing large data sets to find valuable patterns is done using machine learning, advanced algorithms, and computer programming language.

**You can analyse large data sets using data processing tools and software such as Python**. However, the statistical method is always used alongside data processing software to analyze data to get new and meaningful insights.

Analysing data involves:

- Carrying out research and survey
- Collecting data
- Sorting data
- Drawing out possible questions and valuable insights from data sets that can be applied to real-life situations

As a data scientist, you must know and work with different types of data analysis methods. Some of them are as follows:

- Hypothesis testing
- Exploratory analysis
- Quantitative analysis
- Qualitative analysis
- Descriptive analysis
- Predictive analysis

Data sets differ, so different data sets require different approaches for analysing them. Data sets usually need more than one method of analysis. It is the responsibility of a data scientist to understand the data set to know the suitable analysis for the data set.

Look up our other article to learn more about data analysis methods!

The importance of data analysis in our digital world can’t be over-emphasized. It is essential in different fields of work, and it is important in business, finance, education, medicine, scientific research, and more.

Knowing different data analysis methods helps you understand what to expect and what not to. In addition, **analysing data enables you to make possible predictions and draw new and valuable insights from your data model.**

## Solving Statistical Problems

One of the things you should look forward to when learning data science is solving problems. First, you should be excited about them. As an aspiring data scientist in Nigeria, you should be familiar with and solve both basic and advanced statistical challenges.

To solve statistical problems, you must have a basic knowledge of mathematics. You must have done this during your introduction to the statistics course.

To be good at solving exercises, you need to search for examples that have already been solved. This will enable you to know the steps to take when you see a similar statistics question.

You can also solve lots of questions as much as possible, which will help you become familiar with all possible questions.

Solving statistical exercises involves:

- Knowing the aim of solving that problem
- Collecting the appropriate data sample
- Analyzing the big data sets
- Interpreting the results

To solve some problems, **you might have to go through common tests like the chi-square and the T-test.**

Knowing basic calculations such as mean, median, mode, range, variant, standard deviation, and more is essential. You must know all these things before venturing into data science.

These basic calculations will enable you to know when and how to apply them when needed in advanced statistical exercises. To solve fundamental statistical exercises, there are terminologies you need to know and understand, and these terms include:

- Population
- Data
- Sample
- Parameters
- Variables
- Coefficient
- Frequency distribution
- Hypothesis testing

**You can check for experienced Nigerian tutors on a website like Superprof to aid you through your learning process**.

## Finding a Statistics Tutor

Being a good statistician or data scientist requires proper education on the course. To become good in statistics and data science, you have to find an experienced statistics tutor.

Whether you’re new to the course or are willing to learn more about the course, you need resources and an ideal teacher who is good in both statistics and data science. This will enable you to become a great data scientist.

**You have a role to play in choosing your teacher when studying versatile courses such as statistics and data science.**

Before getting a tutor, the first thing you need to do is select the data science program you’re interested in. You can do this by comparing data science courses. You can also check out the IBM data analysis course online.

Studying statistics could involve going for physical or online lessons, depending on what you prefer. For advanced learning, you can opt-in for university education.

When searching for the right teacher for your statistics lessons, you must look out for things. Some of these things include:

- Level of education
- Qualification and certification
- Your schedule and availability of the tutor
- Online reviews from previous clients
- Distance (for physical lessons)
- Level of patience with students (you could get this from reviews)

If you’re looking for professional tutors, **you can check the Superprof website**, where you will find a wide range of Nigerian tutors that are good in these fields.

## How to Become a Data Scientist

Becoming a data scientist is a personal decision, but you need to consider things before opting for the course.

To become a data scientist, you have to know if data science is right for you. You must also know the data science jobs available in the labour market. Knowing this will help you understand what to focus on while saving your time and money.

**Some aspects of data science involve complex mathematics calculations**, and others focus on analysing data qualitatively.

To choose the aspect of data science to focus on, you need to compare job options for data science to see those that suit you. The following are some of the areas where data scientists are expected to be experienced:

- Statistics
- Mathematics
- Python
- SQL
- Hadoop
- Data preparation skills
- Analytical skills

For more information on statistics, data science, machine learning, programming language, and tools for analysing data, you can check out e-books and resources online.

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