DEV Community: Anastasiia Leskiv

How to get a job in data science ?

Anastasiia Leskiv — Wed, 25 Oct 2023 19:09:47 +0000

Getting your first data science job might be very challenging. Having perfect skills and expertise in fields like mathematics, statistics, data analytics, machine learning, modeling, programming, etc might not be enough because each company wants you to have that experience. But what would help you to get that experience? What would help you to get your first job? Let me give you a few tips.

You have to demonstrate your skills to your potential employer. Even when you have a perfect resume it is not enough, having a portfolio of public evidence of your data science skills can do wonders for your job prospects.The ability to show potential employers what you can do instead of just telling them you can do something is very important.

A strong portfolio can make a significant difference in your job prospects and how potential employers perceive your capabilities. Here are some key points to consider when building a data science portfolio:
Show your Projects: Show your practical skills. Talk about your projects.Your projects should ideally cover a range of topics and demonstrate your ability to solve real-world problems. Give employers some examples of how you would use your skills in real world problems.

GitHub: Use GitHub or a similar platform to host your code. Ensure your repositories are well-organized, have clear documentation, and include a README file explaining the project, codes, data, visualizations and results. This makes it easy for potential employers to evaluate your work.

Data Visualization: Data visualization is a key part of data science. Include compelling visualizations in your portfolio that effectively communicate insights from your data analysis.

Publications: If you've written articles, blog posts, or research papers related to data science, include links to them in your portfolio. This can provide evidence of your ability to communicate complex concepts.

Open Source Contributions: Contributing to open-source ventures not only underscores your proficiency but also signifies your ability to collaborate within a team and adhere to best practices in software development.

Continual Learning: Emphasize your commitment to ongoing learning by detailing online courses, certifications, or workshops you've completed. Numerous online platforms offer certificates that can be linked in your portfolio.

Feedback and Improvements: Be open to feedback and iteratively improve your projects. Showcase that you're constantly striving to enhance your skills and create better solutions.

Soft Skills: Alongside technical skills, mention any soft skills you've developed, like problem-solving, communication, and teamwork. These skills are important in a professional setting.

Selecting a project wisely is a crucial aspect of building a strong data science portfolio. When making your choice, it's important to take into account your current skills and experience. Opt for a project that aligns with your proficiency level to ensure a successful outcome. Moreover, your personal interest and motivation are pivotal factors. It's better to pick a project that interests you and fuels your drive to see it through to completion. Feel free to explore alternative datasets that pertain to your preferred field or area of interest, as this can make the project more engaging and rewarding.

Here are more tips for creating a strong data science portfolio:

Focus on Impact: Highlight the real-world impact of your work. Explain the problem you addressed and articulate how your project benefited users or contributed to the business. This helps potential employers understand the value of your contributions.

Document Your Process: Provide a comprehensive account of your project's journey. Include a brief description of the project's context, the data sources and datasets you utilized, the methods and techniques you applied, and the results or insights you achieved.

Publish Your Code: Make your code accessible in your portfolio. Sharing your code allows potential employers to assess your coding skills, problem-solving approaches, and the quality of your work. It's an opportunity to showcase your technical abilities.

Clarity and Conciseness: Present your work in a clear and concise manner. Use data visualizations, storytelling techniques, and simple explanations to effectively communicate your findings. Remember that your portfolio may be viewed by both technical and non-technical audiences.

By incorporating these elements into your portfolio, you'll create a compelling narrative that not only demonstrates your data science expertise but also shows how your work can drive positive outcomes for businesses or users. This can significantly enhance your job prospects and make your portfolio more impactful.

Hope you'll find your dream job soon!

The most important skills for data scientist

Anastasiia Leskiv — Thu, 24 Aug 2023 14:41:53 +0000

Data scientist is plays very important role nowadays. This career combines math and statistics, specialized programming, advanced analytics, and machine learning, all of these helps data scientists to ask and answer questions like what happened, why it happened, what will happen, what can be done with the results, and what what affects the result.

I want to talk about important skills in data science. Of course the most important skills are technical, so, what are they ?

Programing
Programming languages such as Sql, Python, Javascript are very important in data science, it helps to explore, sort, and analyze large dataset easily. Coding is important it creates analytical models and algorithms that allow you to solve complex problems.
Statistics and probability
Those two aspects also important it helps in machine learning models a lot. You have to be able to organize, interpret, and present your data, and for presenting it you have to understand the concept of mean,mode, median, standard deviation and statistics overall. One of the very important techniques in statistics is Probability distributions, function that describes the probability of different possible values of a variable.
Data scientists that uses statistical and probabilistic knowledge may forecast trends and identify anomalies in data sets, create a relationship between two points in the data, and interpolate missing data points.

3.Machine learning and deep learning
Big and very important subject in data science. You want to spend a lot of time learning it.Using machine learning methods can help you become a better data scientist since you'll be able to collect and combine data more effectively while also forecasting the results of upcoming data sets. Using linear regression you can predict how much would be the cost of the house based on the information from the past. Later, you can expand your understanding to incorporate more complex models like Random Forest.
In machine learning there are algorithms you should learn:
-Logistic regression
-Linear regression
-Decision tree
-Random forest algorithm
-K means algorithm
-K-nearest neighbor

4.Data visualization
As a data scientist you must have very strong visualization skills, being able to create charts and graphs is important it helps you present your work to stakeholders. Without visualization even if you did a perfect job but if the clients and stakeholder don't understand it, it’s useless. So, the data must be presented in easy-to-use formats that the average layperson can understand which is visualization.

Interpersonal skills Communication skills very, very important you’ll want to develop this skill to build strong relationship with your team, members, and clients to be able to present your work to stakeholders. Interpersonal skills include :
Listening
Communication
Leadership
Attention to details
Public speaking
Sharing feedback
Be able to convey your opinion

It can be difficult way to become a data scientist because it takes a variety of technical abilities, subject-matter expertise, and real-world experience.But nothing is easy.If you want to achieve something and you put all your effort into it, you will definitely succeed. Numerous fields, including mathematics, statistics, programming, and machine learning, are included in this career. However, it is possible to develop the essential abilities and be successful as a data scientist with commitment, ongoing learning, and practical experience.

How do you know if Data Science is a good fit for you?

Anastasiia Leskiv — Thu, 29 Jun 2023 23:35:12 +0000

Are you thinking of a career change and wondering if Data Science is a right for you?
Data scientist is a fantastic career with a tonne of potential for future growth.
Start with asking yourself, are you a curious person? Analytic is about being a detective, data detective. You have to find the answer which might be hidden and not always easy to find. You have to be ready to work hard and look deep to find the right answer. You have to be ready. Sometimes you are not sure where to find the right answer, there will be a lot of mistakes. Finding the answer takes a lot of tries and errors. You have to be confident in your ability to solve the question even if it takes a certain amount of errors and fails before? Then you are a good fit for this career.
Ask yourself: Do you have a good logical approach to work? Good Data Scientists have an analytical mindset. Data science is about logical thinking, generating more ideas and creativity in solving the problems.
Are you passionate about problem-solving?
Data science is all about problem-solving. Good Data Scientist is a curious person who likes to solve problems, likes puzzles, and is inspired when learning something new.
Are you interested in strategy?
You have to find the data and help the business find its strategy. It will be one of the primary responsibilities.

Don't forget about statistics.
You don't need a big math background. You have to be comfortable working with numbers and statistics. It's a very important part of this job.
Are you comfortable presenting your work to the audience and collaborating with them?
You have to present your work to a stakeholder with a non technical presentation so everybody can understand. You have to explain your strategy and help the business take action. If you are a comfortable presenter and ready to argue your case, then you are a good fit for you.
After answering these questions you still think this is for you and exactly what you want to get involved in? - great. Data is not going anywhere neither Data Analytics/Scientists, so, this is an excellent job market to be a part of. You will feel great to be a part of the decision making team. Data Analytics/Scientists play a crucial role in making a strategy for the business.
The beauty of being a Data Science is that you have a variety of industries to be in from health care to real estate, from fashion to marketing, technology ets. Data in everywhere if you like to have a variety. Data Science is your best option. I hope my post was interesting, informative, and inspiring.

What you should know about Data Science to get started.

Anastasiia Leskiv — Fri, 14 Apr 2023 23:06:50 +0000

Data science is a very trendy topic/profession nowadays.A lot of people are interested in this field but really not sure what Data science actually means. This article is all about what is Data science and why it is important for a company to have one.
So, let's start with the really first question you ask yourself when you hear about this field. What is Data Science?
The definition you can see on Google would be something like Data science combining domain expertise, programming skills, and knowledge of mathematics and statistics to extend manful insight from data. Which is a good definition but what does exactly that mean?

When it comes to Data Science there are 5 main stages.

Capture
This is the process of extracting information from any type of documents and converting it into a format readable by a computer.
Maintain
That is when we are cleaning, correcting, ingesting, storing, organizing and maintaining the data created and collected by an organization.

3.Process
Systematic approach to solving a data problem. Choosing a strategy we will use to solve the problem.

4.Analyze
Process when applying statistics and logic to describe and illustrate, condense and recap, and evaluate data.

Comunicate When we do data visualization, making decisions, and reporting our findings to clients or business people in order to help them make strategic and business decisions.

The second very important question you should ask is what problems can data science solve?
There is 4 main problems that data science can solve:

Regression
Regression is answering the question: how much or how many? It used to predict a continuous value. For example, what would be the sale price of a house?
Classification
Classification is answering the question: which category? It used to predict which category something will fall into.If you're trying to figure out whether a client is likely to default on a loan or which of your products a customer is likely to prefer, you're dealing with a classification problem.
Anomaly detection
Anomaly detection is answering the question: if this is weird? It identifies fraud and is used to find unusual patterns that do not conform to expected behavior.

4.Recommender systems
Recommender systems answer the question: which item would a user prefer? It used to predict user preferences towards a product/service. For example when we watch Netflix we can notice a section like "recommended for you because you watched ..." This is exactly what recommender systems are.

The work data scientists do is very interesting and helpful. It helps to improve businesses and first of all quality of lives. In my opinion data science will always be relevant, in the future it will be even more necessary for every company since we live in the technical world and there are no limits in development. I hope my blog was interesting, informative, and inspiring.