Skills to succeed as a Data Scientist

So, what’s the big deal? 

Artificial intelligence (AI), according to experts, will impact people’s lives in the next years. AI will, in the end, affect the planet more than anything else in history. 

Everyone appears to be talking about Data Science and Artificial Intelligence, whether it’s in media stories, job listings, or interviews with senior executives from companies like Google, Facebook, and Microsoft. If you’re like the majority of them, you’re probably wondering how to become a Data Scientist. 

So it’s time to get serious about it. Data Scientist was named the Sexiest Job of the Century by Harvard Business Review in 2012. It has become a very profitable career option for students and software experts owing to the buzz and demand around it. 

What is Data Science, exactly? Is it simple to train as a Data Scientist or an AI expert? 

Even the most experienced specialists have difficulties articulating the extent of the topic, so if you know what Data Science/AI is, you are in the minority. One possible distinction is that a data scientist is someone who uses machine learning and statistics to create predictive and/or explanatory models. 

Using advanced machine learning & deep learning techniques, mathematics & statistics, programming & technology, data science is all about identifying relevant insights (usage, trends, behaviour, retention, and results). 

 What skills do you need? 

Here’s an issue for you to solve if you enjoy doing just that: What would you do if your profession became outdated in the next ten years? Would you rather learn new skills or work harder in the same position? Before you answer, keep in mind that Data Scientists earn 50 percent more than other IT workers on average, thanks to a 417 percent rise in demand for Data Scientists across all businesses in the previous year. As a result, you don’t need to be a manager or a leader to make a lot of money; simply learning the best data scientist skills would be enough. 

As appealing since it may seem, data science is a tough sector to break into, because it necessitates a number of strong prerequisites in multiple domains. People with strong programming abilities, statistics and mathematics knowledge, domain knowledge, and a passion for data have a high chance of becoming Data Scientists or AI experts. 

 What do Data Scientists/AI Experts do and who are they? 

Given the vast range of tasks that data scientists do, there tends to be some misunderstanding about their roles. Are they a statistician, a mathematician, or a software developer? 

The following pretty much sums this up: 

A data scientist is a software engineer who is better at statistics than any statistician. Similarly, a software engineer is better at statistics than any statistician. 

 Here are a few activities that data scientists are frequently asked to complete: 

• Identify and construct a data analytics-based issue statements that can directly benefit the organisation or its clients. 
• Gather, clean, convert, analyse, and process structured and unstructured data from a variety of sources. 
• To undertake an in-depth analysis of processed data, a data scientist must create statistical models and, if necessary, apply machine learning methods. 

• Interpret data models to locate trends, answers, and possibilities for the organization’s development and difficulties. 
• Stakeholders should be able to comprehend the data outputs. One of the most critical talents a data scientist must have is storytelling and visualisation. 

 What qualifications are do you require to work as a data scientist? 

A strong data scientist’s skill set includes modules in data mining, data analysis, programming, mathematics and statistics, machine learning, business, data hacking, data visualisation, database & (big) data. 

 Let’s look at a few of them more closely. 

 

R and Python programming fundamentals 

Different concepts, algorithmic flow topologies, and paradigms are implemented in various programming languages. However, the objective is to get wide knowledge of such structure and principles rather than to become a master of any one language. Once you’ve completed these, it’ll be much easier for you to take up any programming languages you choose to study. 

Python, R, SAS, SPSS, Perl, and SQL/NoSQL are the most crucial programming languages and technologies to know or master if you want to succeed in this industry. 

Python and R are two of the most popular programming languages for data science and AI. 

 

Artificial Intelligence (AI) 

As a Data Scientist, one of your primary roles is to identify business challenges and convert them into Machine Learning tasks. You can apply your Machine Learning talents to add data into the algorithms when you acquire datasets. ML will use data-driven models and fast algorithms to handle these data in real-time. Soon, the computer will be able to recognise and forecast the data pattern, resulting in precise outcomes. 

If you work in a huge data-driven company, you’ll need to be familiar with ensemble techniques, random forests, and k-nearest neighbours algorithms, among other things. 

 

Statistical data (Descriptive and Inferential) 

When summarising a large amount of data, descriptive statistics is useful for helping to explain or summarise data in a meaningful way. For example, if we obtain the coursework results of a group of 100 students, we may be able to describe their overall achievement. The sample refers to this group of pupils. Descriptive statistics can assist us in this quest. 

Inferential statistics uses data from a sample to draw conclusions about the population from which the sample was drawn. The purpose of inferential statistics is to come to a conclusion based on the sample results. 

 Aspiring AI experts must understand the fundamentals of descriptive and inferential statistics. 

 

Calculus and Linear Algebra 

If you understand the concepts of linear algebra and calculus, you may make little changes to the method that will have a large influence on the eventual outcome. Though it is not necessary to master them, a few firms that generate a lot of data, such as Netflix, Amazon, and others, constantly hunt for Data Scientist applicants with strong linear algebra and calculus expertise. 

 

Data Wrangling 

The data you evaluate as a Data Scientist is frequently complicated and difficult to deal with. As a result, it’s critical to know how to deal with inaccuracies in a dataset. Data that has been corrupted may be missing certain normal values or may not be in the correct format. 

You can use data wrangling to remove damaged data and organise it properly. One of the most crucial talents of a data scientist is the ability to process and use data for analytics. 

 

Visualization of Data 

Stakeholders must be able to discuss data in order to make data-driven choices. This implies you must explain how your results benefit the ultimate audience, which includes both technical and non-technical experts. As a result, data visualisation abilities, which include data visualisation coding and information transfer, are required. 

You can get started with data visualisation by learning how to use programs like Matplotlib, ggplot, and Tableau. 

Big Data  

Data Scientists work with a wide range of data types, including both organised and unstructured databases. They clean, sort, and manage them using their data wrangling, programming, and other talents as a data scientist. They will be able to unearth the hidden answers to upcoming business difficulties in this manner. As a result, as a Data Scientist, you must be able to interface with Big Data and understand how to acquire, manage, and analyse it. 

Data Intuition 

When you work as a Data Scientist, the company expects you to be a problem solver who can come up with the best answer to a problem. In this situation, you’ll need to think about what’s important, what’s not, and how to work with engineers, stakeholders, and even end-users. So, how are you going to handle all of this? 

Whether you call it business intuition or data intuition, the most important thing to grasp is how to apply your data scientist abilities and knowledge of arithmetic, statistics, programming, Big Data Analytics, and so on to come up with the best practical answer. 

Machine learning 

 Machine learning is a technique for teaching computers to learn and improve on their own when fresh data is fed into them. In today’s world, recommendation engines, self-driving vehicles, recruiting firms, and other businesses rely significantly on machine learning to improve their user experience. 

 To clarify, machine learning is a subset of artificial intelligence. Machine learning enables businesses to automate critical procedures in real-time, lowering the cost of activities that rely on human interaction. Data scientists should be familiar with machine learning since it aids them in developing systems that can generate high-value predictions and make choices in real-time. 

 Collaboration 

To become a successful Data Scientist, you must channel your Data Science knowledge into accelerating the speed of output in order to assure your company’s long-term success. You can’t do it on your own. You should ideally work with your team (technical and non-technical), stakeholders, and end-users to achieve your goals. As a result, if you have the necessary people skills, you may work with others to identify their pain spots and solve organisational problems. 

  

Many people enter this industry without having the necessary statistical, machine learning, or analytical abilities. You must enroll in a well-designed Data Science Course to avoid this and to take advantage of the current Data Science prospects. 

Get in touch with TrainOn to know more about the Data Scientists’ training program.