DEV Community: Tony Ndereva

"Understanding Your Data: The Essentials of Exploratory Data Analysis". #exploratory data analysis #data science # statistics

Tony Ndereva — Sat, 17 Aug 2024 22:39:25 +0000

Exploratory Data Analysis (EDA) involves investigating datasets to understand the variables in the data set and their relationships better through visualization and summary statistics.

EDA enables data scientists to spot anomalies, get a picture of the dataset through summaries and visualizations and avoid making inappropriate assumptions. Additionaly, vairables identified in EDA can be used later in machine learning to build predictive model.

Types of EDA
There are 4 types of Exploratory Data Analysis:

Univariate non-graphical: This methods involve the use of statistics to obtain various descriptions of a dataset with only one variable(univariate)

2.
Univariate graphical: This involves the use of graphical methods such as stem and leaf plots and box plots to visualize one variable data making it easier for the scientist to understand the dataset.

3.
Multivariate non-graphical: This method uses statistical methods such as correlation, covariance and regression to identify the relationships between different variables in a dataset. For example, the relationship between housing and inflation can quantified using correlation.

4.
Multivariate graphical: This makes use of various graphical methods such as scatter plts and regression lines to visualize the relationship between various variables. This aids in the identification and understanding of these relationships by the data scientist.

Tools in EDA
Apart from a good grasp of statistics, computer languages such as Python and R come in handy in exploratory data analysis.

Expert advice on how to build a successful career in data science, including tips on education, skills, and job searching.

Tony Ndereva — Sun, 04 Aug 2024 02:30:58 +0000

"Data is the new oil!" This is a phrase that has been spoken so many times that some might find it cliche. What really is data science? How do I become one? What skills do I need? I explain all this in the next paragraph.

Data science is a relatively new field in the job market. It can be defined as the process of using data to derive insights which make an impact to business and the world in general. Data science involves using mathematical and statistical methods find insights that make a difference to a company's bottomline.

What educational background should I have to be a data scientist?

The field of data science involves using statistical methods to convert data into helpful recommendations for businesses. Therefore, a background in computer science, mathematics or statistics is usually a good starting point for anyone who wants to be a data scientist. However, skills might sometimes outweigh the education background for a career in data science. So what skills does a data scientist need you ask? I have elaborated the various skills and technological tools needed for a successful career in data science in the next section and tips to secure a job in the data science field.

Skills needed in data science

In order to thrive as a data scientist, you need to be able to not only wrap your head around various mathematical concepts but also have technological skills and soft skills in your arsenal.

Some of the mathematical concepts needed in data science include but are not limited to:

Linear algebra: Data science involves the use of vectors, matrices and linear transformations to manipulate and analyze data. Since linear algebra is the study of these very concepts, knowledge in linear algebra is a fundamental skill for a successful career in data science.
Probability and Statistics: A good grasp of statistical concepts such as regression models, correlations and tests of hypothesis to name a few. These aid the data scientist to test and validate or reject various ideas.
Calculus: Calculus concepts are used in data science to solve optimization problems and estimate various variables of interest.

The technological skills needed in data science include but are not limited to the following:

Programming: Since data comes in various formats, the collecting, cleaning and manipulation of various data formats is needed. A data scientist should be well versed in various programming languages such as: Python, SQL and R. Proficiency in these languages helps one tackle the various stages of data processing.
Tools: A data scientist uses tools to enable them to not only simulate,build and validate models, but also store data as it is being processed. Some of the tools used for these purposes are:
Database management systems: PostgreSQL, Snowflake, Microsoft SQL server e.t.c. for the storage of data and manipulation
Visualization tools: PowerBi, Tableau and Excel are examples of some popular visualization tools in data science
Big data techologies: Since more data is created each and every passing second, technologies are needed to handle such large amounts of data. Some big data technologies include: Hadoop,Kafka and Spark.

-Cloud technologies: Cloud technologies are essential in cloud computing.It is advisable to have either one of the following skills: Microsoft Azure, Amazon Web Services or Google Cloud Platform.

Soft Skills in data science

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Data scientists do not work in a solitary environment. They have to report findings to management, collaborate with fellow data scientists and team up with data analysts and data engineers to achieve their mission. It is therefore imperative for dat scientist to have skills such as teamwork, collaboration, and public speaking to be able to articulate ideas and findings in a clear manner.

Tips to securing a job in the data science field.

Be loud: Explain your work in meetups, communities an other professional gatherings to make potential employers aware of your prowess. -Have a portfolio: Having a portfolio of projects you have carried out enables recruiters to gauge your skills and showcases your areas of expertise. -Networking: A majority of job vacancies are filled through word of mouth. It is useful to know people in your field of work. They might mention your name when vacancies come up and refer you to various companies. -A solid resumes: Resumes help recruiters understand the type of person they are hiring. Having a properly formatted resumes enables recruiter understand you better and find out more about your skills. -Finallly, do not give up! Keep sending those applications and building your network and you will find people who need your expertise.

Success in your data science career!