Skills That Will Help You Land A Job In Data Science

Data visualization, as the name implies, is the capacity to present data findings using graphics or other visuals.

The goal is simple: it allows even those who aren’t trained in data analysis to gain a better understanding of data-driven insights. 

Data analysts can use data visualization to assist business decision-makers in seeing trends and comprehending complicated ideas at a glance. This functionality allows you, the data analyst, to obtain a deeper grasp of a company’s position, communicate relevant findings to team leaders, and even influence company decision-making.

Data visualization may potentially enable you to achieve more than traditional data analysts have been able to. The way our analysts work with data will transform due to data visualization. They’ll be expected to respond to problems more quickly, and they’ll also need to delve deeper for fresh insights – to look at data in new ways and think outside the box. Data visualization will encourage more innovative data research.

Data visualization is a valuable skill nowadays, as more and more companies involve their business department with IT. It’s great to have experts gathering all the most helpful information, but it needs to be understood by the users’ clients and colleagues who need to be kept in the loop.

Ravi Parikh of RoverPass briefly pointed out that “Data visualization with Tableau & Excel is also useful to have. Selecting Data Visualizations For Your Data Science Projects

1. Identify the objective of your project.
2. Understand your audience.
3. Get an idea of your data type– ordinal, nominal, categorical, and qualitative.
4. Choose the chart type– bar chart, pie chart, area chart, scatter plot.
5. Look for the best data visualization tools

Data science, statisticians joke, is merely a hyped-up version of statistics, a profession that has been around for decades. There’s some truth to it as well. 

Data scientists may use coding languages and machine learning models that statisticians could only dream of in the past, but it’s all statistics behind the hood. To become a data scientist, you don’t need a PhD in mathematics, but you need a thorough understanding of probability and statistics. This will assist you in determining which types of analyses are acceptable and evaluating your results to ensure that they are correct and meaningful. 

In other words, statistical expertise is the difference between knowing and hoping that your conclusion is correct.

Olivia Tan of CocoFax further explains, To write correct sentences, you must know grammar. Similarly, you need to know statistics to build good models. Machine Learning evolves from statistics, and even the concept of linear regression has been known for a long time. According to Wikipedia, statistics collect, analyze, interpret, present, and arrange data. 

So, it’s no surprise that data scientists need statistical understanding. Descriptive statistics concepts like mean, median, mode, variance, and standard deviation are required. After that, there are inferential statistics like hypothesis testing and confidence intervals, as well as sample and population probability distributions.

Ankush Sharma, CEO, Co-Founder of DataToBiz, throws light on the essential programming languages. He says, “Python, along with Java, Perl, and C or C++, is the most prevalent coding language required in data science employment. For data scientists, Python is an excellent programming language. This is why, according to O’Reilly’s survey, 40% of respondents use Python as their primary programming language. 

Python’s adaptability allows it to be used for practically all phases required in data science procedures. It supports a wide range of data formats and easily imports SQL tables into your code. It allows you to build datasets, and Google can practically discover any form of the dataset you need.

The ability to use machine learning and artificial intelligence to your advantage (AI). In most firms, neither machine learning nor artificial intelligence will be able to take your position. On the other hand, leveraging them will increase the value you provide as a data scientist while also allowing you to work more efficiently and effectively.

He adds, “To fully exploit the potential of artificial intelligence and machine learning, you’ll need several abilities that are uniquely human. The most difficult challenges in artificial intelligence are knowing whether you have the appropriate data, recognizing when the ‘right data’ displays the wrong stuff, and identifying good enough data for AI.

Michael Butt of Verta.ai puts emphasis on SQL and suggests, You must learn SQL regardless of which programming language you choose. SQL, which can be pronounced SQL or sequel, is known as a query language. It’s essentially a specialized programming language for requesting and filtering information from a database. SQL can be used to read and retrieve data from a database and update and insert new information. Creating a SQL query is usually the initial step in any evaluation process. 

Aspiring data scientists often neglect SQL, but it is a necessary skill for data science because most businesses keep their data in SQL-based databases.

Victoria Mendoza from MediaPeanut explains how being communicative works in favor of Data Scientists. She says, “In terms of non-technical skills, I think the most important is to have communication skills. Data scientists are well aware of extracting, comprehending, and evaluating data. 

However, for you to be successful in your work and for your business to profit from your services, you must effectively communicate your findings with team members who do not share your professional experience. The only way to receive a decent value for your labor is to be able to demonstrate how insightful the results are and how they can assist improve revenues and the company.

Victoria Mendoza continues talking about non-technical skills and explains how being intuitive works out. She points out, “An intuitive mind and someone with curiosity is what is essential in a data science job. In enormous data sets, valuable data insights are not always obvious. A trained data scientist needs intuition and understanding of when to go beneath the surface for insightful information. One of the most important soft skills of a data scientist is the ability to ask questions regularly.

John Fordice, Analytics Leadat Bonsai says, “Bugs, problems, and roadblocks are something you will always run up against as a data scientist, and you should have the capability of solving it.

Ankush Sharma believes that a certain level of business acumen is essential for a Data Scientist to efficiently use data in a way that is valuable to their organization. You must fully comprehend the company’s core objectives and goals and how they affect the work you accomplish. You must also be able to develop solutions that achieve those objectives in a cost-effective, simple-to-implement manner that ensures broad acceptance.

Daniela Sawyer, Founder of FindPeopleFast, says, the most important thing to get a job in the data science profession is storytelling. Remember, Statistical computations are ineffective if committees can’t work upon them. Storytelling skills stand crucial in oral communications jotting down and data visualization. Storytelling implies that analytical solutions are communicated in an apparent, clear, and on point.

One of the most important things is a background in math (linear algebra, calculus, statistics, and probability theory) – it greatly helps to structure problems, work with algorithms and have a precise understanding of what is happening.

Another crucial skill is coding, and it’s hard to overestimate its importance. While a background in math helps to think correctly, coding helps to do all the implementation work at speed and focus on hypothesis design, metric analysis, and experiment setup instead of struggling with infrastructure every time a new idea arises.

Data science is a field breathing and growing. There are a lot of
misconceptions that companies change their analysts’ title to data
scientists. In the long run it doesn’t help the company as the analysts are
not able to run advanced techniques on data. It is also bad for analysts as
they cannot show their skills in the next job beyond basic analysis. There
is nothing wrong about being a good analyst or having an army of analysts.

Data Science is a stable career path, but it can be difficult to break into the field due to the rigorous interview process required for most roles. Employers will expect candidates to be proficient in statistics, computer science, machine learning, and business intelligence competencies.

To become a data scientist, potential candidates should understand statistical concepts like hypothesis testing, conditional probability, and common probability distributions (Normal, Binomial, Exponential, etc.).

They should also have fundamental computer science skills such as coding in common computer languages like Python, R and Java, database querying in SQL, and big data processing in platforms like Spark and Hive.

It is also critical to demonstrate a grasp of machine learning algorithms (gradient descent, K-nearest neighbors, K-means, decision trees, boosting, bagging, etc.). Finally, candidates should be prepared to show business intelligence expertise including the ability to communicate quantitative ideas to a non-technical audience. While these necessary skills can seem daunting for a beginner, data science is a rewarding career that allows professionals to practice creativity and quantitative skills on a regular basis.