Data science is a broad term that incorporates data analytics, data mining, Artificial Intelligence, machine learning, Deep Learning, and a number of other related fields. Data Science is undeniably one of the fastest-growing fields in terms of both career possibilities and salary. Data science is a field with a steep learning curve, that is, you get to learn a lot of things in a very short span of time. Data scientists must be fluent in a variety of computer languages and statistical computations, as well as possess good interpersonal and communication skills.

Data scientists can effectively express and communicate complicated statistical insights to a lay audience and make actionable suggestions to the proper stakeholders by combining a solid educational foundation with the right technical and interpersonal abilities.

In this article, we will cover the essential skills required to become a data scientist. Before we see what the top data scientist skills are, let us first understand who a data scientist really is and what their job responsibilities are.

## Who is a Data Scientist?

A Data Scientist is in charge of compiling and analyzing huge, structured, and unstructured data sets. This profession uses math, statistics, and computer science skills to decipher large amounts of data and then apply the information to develop commercial solutions to the problems currently faced by the organization.

In order to generate a detailed report analysis of data and to come up with an optimal solution to the problem being faced, data scientists collect, process, model, and evaluate data utilizing everything from technology to industry trends. They also make certain that the data has been adequately cleaned and confirmed, as well as that it is correct and complete in terms of the problem statement considered. Data scientists are analytic professionals that use their knowledge of technology and social science to identify patterns and handle data. They identify solutions to corporate difficulties by combining industry knowledge, contextual insight, and skepticism of established assumptions. A data scientist’s job entails deciphering unstructured data from sources like smart devices, social media feeds, and emails that don’t fit neatly into a database. From data cleansing to data processing and storage, experienced data scientists are responsible for defining a company’s best practices. They collaborate cross-functionally with other departments such as marketing, customer success, and operations. They are in high demand in today’s data and tech-driven economy, as seen by their wages and job growth.

## Top Data Scientist Skills

### 1. Fundamentals of Data Science:

Understanding the principles of data science, machine learning, and artificial intelligence as a whole is the first and most crucial skill you’ll need. Understand topics such as –

- What is the difference between deep learning and machine learning?
- Data science, business analytics, and data engineering and the differences between them.
- Terminologies and tools that are commonly used
- What is the difference between supervised and unsupervised learning?
- Problems of classification vs. regression

### 2. Deep knowledge of mathematical concepts like statistics and probability:

When learning to write sentences, you must be familiar with grammar in order to construct proper sentences. Similarly, before you can create high-quality models, you need to understand statistics. Machine Learning begins with statistics and evolves. Even the concept of linear regression is a statistical analysis concept that has been around for a long time.

Statistics is defined as the study of the collection, analysis, interpretation, presentation, and organizing of data, according to Wikipedia. As a result, it should come as no surprise that data scientists require statistical knowledge in their profession. It is necessary to understand the concepts of descriptive statistics such as mean, median, mode, variance, and standard deviation. Then there are probability distributions, sample and population, CLT, skewness and kurtosis, and inferential statistics, such as hypothesis testing and confidence intervals.

### 3. Knowledge of Programming Languages:

Data scientists must be proficient in advanced statistical modeling tools and have a deep understanding and knowledge of programming, in addition to having a strong foundation in mathematics and statistics. There are a variety of programming languages that are preferred for the role of a data scientist. Some of them are as follows :

**Python**: Python can handle everything from data mining to website development to running embedded systems in a single language. Pandas is a Python data analysis package that can do everything from import data from Excel spreadsheets to plot data with histograms and box plots. Data processing, reading, aggregation, and visualization are all made simple with this library.

**R Programming:** R is a software package that includes functions for data manipulation, calculation, and graphical display. In comparison to Python, R is more widely used in academic environments. Machine learning algorithms may be implemented fast and easily, and the software includes a number of statistical and graphical approaches, including linear and non-linear modelling, classical statistical tests, time-series analysis, classification, and clustering.

### 4. Experience in Data Extraction, Transformation and Loading:

Assume we have several data sources, such as MySQL, MongoDB, Google Analytics, etc (examples of different databases available). You must extract data from such sources and then transform it so that it may be stored in a suitable format or structure for querying and analysis. Finally, you must load the data into the Data Warehouse (a type of data management system designed to enable and support Business Intelligence activities, particularly analytics), which will be used to analyze it. Data Science may be a suitable career choice for persons with an ETL (Extract, Transform, and Load) background.

### 5. Knowledge of Data Wrangling and Data Exploration:

Data Wrangling is the process of cleaning and unifying messy and complex data collections for easy access and analysis. Take, for example, the act of packing your luggage. What happens if you stuff your entire wardrobe into your bag? You’ll save a few minutes, but it’s not the most efficient method, and your clothes will be ruined as well. Instead, spend a few minutes ironing and stacking your clothes. It will be considerably more efficient, and your clothing will last longer.

The initial phase in your data analysis process is exploratory data analysis (EDA). Here, you’ll figure out how to make sense of the data you have, as well as what questions you want to ask and how to phrase them, as well as how to best modify your data sources to get the answers to the problem currently being considered. This is done by looking at patterns, trends, outliers, unexpected outcomes, and so on. Data manipulation and wrangling, on the other hand, can take a long time but can ultimately help you make better data-driven judgments. Missing value imputation, outlier treatment, correcting data types, scaling, and transformation are some of the common data manipulation and wrangling techniques used.

So, a data scientist must be familiar and confident in concepts of data wrangling and data exploration.

### 6. Knowledge of Data Visualisation:

One of the most significant aspects of data analysis is data visualization. It has always been critical to convey information in a way that is both understandable and pleasant to the eye. One of the skills that Data Scientists must acquire in order to connect more effectively with end-users is data visualization. There are programs available, including Tableau, Power BI, Qlik Sense, and many others, that have a user-friendly interface.

Data visualization is more of an art than a pre-programmed procedure. There is no such thing as a “one-size-fits-all” solution here. A Data Visualization expert understands how to use graphics to convey a message. To begin, you must be comfortable with basic plots such as histograms, bar charts, and pie charts, before moving on to more advanced charts such as waterfall charts, thermometer charts, and so on. During the exploratory data analysis stage, these graphs are extremely useful. Colorful graphics make univariate and bivariate studies much easier to comprehend.

### 7. Comprehensive Knowledge of Machine Learning:

Machine learning is a must-have ability for any data scientist. Predictive models are created using machine learning. If you want to forecast how many clients you’ll have in the upcoming month based on the previous month’s data, for example, you’ll need to employ machine learning techniques. You can begin with simple linear and logistic regression models before progressing to sophisticated ensemble models such as Random Forest, XGBoost, CatBoost, and others. Knowing the code for these algorithms is useful, but understanding how they operate is more vital. This will aid hyperparameter adjustment and, ultimately, the creation of a model with a low error rate.

### 8. Firm Knowledge of Big Data Processing Frameworks:

To train Machine Learning/ Deep Learning models, a large amount of data is necessary. Creating exact Machine Learning/ Deep Learning models was previously impossible due to a lack of data and computer capability. A large volume of data is generated at a high rate nowadays. Because this data can be organized or unstructured, typical data processing methods are unable to process it. Big Data refers to such massive data sets. As a result, frameworks like Hadoop, Spark, etc are required to handle Big Data. Most businesses nowadays use Big Data analytics to uncover hidden business insights. As a result, it is a necessary skill for a Data Scientist.

### Knowledge of Software Engineering Principles:

To build high-quality code that won’t cause problems during production, you’ll need to understand the fundamentals of software engineering topics including the basic lifecycle of software development projects, data types, compilers, time-space complexity, and so on. Writing efficient and clean code will benefit you in the long run and will make it easier for you to collaborate with your teammates. Again, you don’t have to be a software engineer, but knowing the fundamentals will assist.

Thus, thorough knowledge of software engineering principles is a must for a data scientist.

### 9. Comprehensive Knowledge of Model Deployment:

The most underappreciated stage in the machine learning lifecycle is model deployment. Let’s look at an example. A data science initiative has been launched by an insurance firm that analyses vehicle photos from accidents to determine the amount of damage. They have the model ready after months of hard effort, and the stakeholders are pleased with its performance, but what happens next?

Remember that the end-users in this scenario are insurance agents and that this model will be utilized by several people who are not data scientists at the same time. As a result, they won’t be using GPUs to operate a Jupyter or Colab notebook. This is when a thorough model deployment process is required. Machine learning engineers are normally in charge of this activity, but it differs depending on the company you work for. Even if it is not a job requirement at your organization, understanding the fundamentals of model deployment and why it is necessary is critical.

### 10. Good Problem Solving Skills and Thorough Knowledge of Data Structures and Algorithms:

Data Scientists must have good problem-solving skills and they must be able to quickly analyze any error in the training model and fix them quickly. They must be able to come up with multiple solutions to a single problem. They must also be well versed with advanced data structures and algorithms as they can often be helpful in designing the training model.

### 11. Good Communication Skills:

Data can’t talk unless it’s been manipulated, so a good Data Scientist must be able to communicate effectively. Communication may make all the difference in the outcome of a project, whether it’s communicating to your team what actions you want to take to get from point A to point B with the data or presenting a presentation to corporate leadership. In most data scientist professions, excellent communication skills are required. You’ll need to grasp business requirements or the problem at hand as a data scientist, as well as probe stakeholders for more data and communicate crucial data insights.

### 12. Curiosity and Desire for a steep learning curve:

Data science technologies and frameworks grow at such a rapid pace that mastering any single one is pointless. Rather than striving for perfection, you should focus on developing the patience and discipline to teach oneself new skills and swiftly grasp new concepts. One of the most important soft skills of a data scientist is the ability to keep asking questions. You can follow all of the processes of the machine learning project lifecycle if you’re monotonous, but you won’t be able to attain the final goal and justify your results.

## Conclusion

Being a data scientist in this decade is exciting. There are many opportunities in this field and it is a very promising career. In this article, we have discussed the top skills required to be a data scientist.

## Frequently Asked Questions

**Q1. Is it hard to become a data scientist?**

Ans. Data Science careers have high technical requirements as compared to other technical fields. Getting a strong grasp on such a diverse range of languages and applications is a steep learning curve. However, nothing is hard if one has the determination and willingness to do something. So, go ahead and explore a career in data science as it is a very promising career.

**Q2. Which degree is best for a data scientist?**

Ans. To get your foot in the door as an entry-level data scientist, you’ll need at least a bachelor’s degree in data science or a computer-related discipline, however, most data science jobs will require a master’s degree.

**Q3. How long is the data science course?**

Ans. Bachelors in data science are three to four years of undergraduate data science courses in the domains of engineering and sciences.

**Q4. What is data science eligibility?**

Ans. Class 12 (for bachelors in data science) with 50 percent aggregate marks and clarity of basic mathematics and statistics concepts (Probability, Calculus, Algebra) are the basic data science course qualifying criteria.