Data Scientists solve the industry’s trickiest challenges that require them to be smart, knowledgeable, and logical, and analytical in their approaches. Thus, they need to be adept in multiple disciplines.

Data Scientists perform one of the most challenging jobs in the industry. Working in data science requires an extensive set of skills. It is a multi-disciplinary field that requires strong analytical and logical skills with a strong foundation in statistics, machine learning, mathematics, and other numerical-focused areas. Thus, to enter **data science industry**, you need to learn a number of skills. If you’re an aspiring Data Scientist, this post lists various skills required in Data Science.

Essentially, Data Science merges skills in computer science, statistics, programming, and mathematics. Online data science courses like those found at Le Wagon can set you on the right path.

Without any further ado, let’s get down to the skills you will need to become a Data Scientist.

**Skills Required in Data Science **

1. **Statistics **– It forms the backbone of Data Science. Before Data Science, statisticians were employed by government agencies and enterprises that worked with large amounts of data. Statisticians were involved in understanding the data and making sense of it. They use statistical tools to describe data and make judgments. Majorly two aspects of statistics which are important to Data Science:

- Descriptive statistics
- Inferential statistics

### **Descriptive Statistics**

Using descriptive statistics, Data Scientist can describe data. It is a quantitative summarization of available data using graph and other numerical representations.

Some key concepts in descriptive statistics are —

- Normal distribution – A normal distribution is used to describe data that is evenly distributed through its observation. On plotting data on a graph paper, the asymmetrical bell curve is formed. Inferential statistics apply to normally distributed data.
- Central tendency –The central tendency has three tools to describe data – mean, median, mode. Mean is the sum of data samples divided by the number of data samples, while the median is the middle data when arranged in ascending order. In the case of an odd number of data samples, the median is the middle number. However, in case of even numbers, the average middle numbers are taken as the median. The mode is the most frequently occurring number in the data sample.
- Skewness – Skewness is the measure of lack of symmetry in data. For uniform data, skewness is zero. The graph of data with skewness equal to zero would reflect a normal distribution, while positive skewness would be data stacking on the left side. For negative skewness, data will stack on the right side when plotted on a graph.
- Kurtosis – Kurtosis reflects tailed-ness in data. It measures tailed-ness concerning central distribution. High tailed-ness means data is highly trailing from the central distribution.
- Variability – Variability is the measure of the distance of data points from the central distribution of data. It is measured in three ways – range, variance, and standard deviation. The range is the difference between the highest and lowest data points. Variance is the difference between the sum of squared data points and mean. The standard deviation is the square root of variance.

Next is inferential statistics.

**Inferential Statistics**

It is the most functional part of data science utilized in decision making. Using inferential statistics, you derive inferences and conclusions from available data. To understand this, here’s an example —

Say, you want to measure the number of cellphones in a country. For this, you could go throughout the country and ask. Or you can take a sample group of people and calculate the number of cellphones in the group. To calculate the total number of cellphones, multiply the mean number of cellphones by the total population.

Following are major concepts in inferential statistics —

- Central limit theorem – According to this, the mean of the sample size is the mean of total samples. It is a commonly used tool for data analysis.

- Hypothesis testing – Hypothesis testing is a way to test the assumption. In practice, there’s a Null hypothesis, which needs to be tested against an alternative hypothesis. Data related to both hypotheses is collected and analyzed. Based on the analysis, a hypothesis is rejected.

- ANOVA – ANOVA is a hypothesis for multiple groups. A null hypothesis is set, where the mean of all the groups is considered the same, while the alternative hypothesis is the mean of all groups is different. F-ratio is used to measure ANOVA. F-ratio is defined as the ratio of the Mean Square between groups to the mean square in groups.
- Quantitative analysis — Correlation and Regression are two techniques of quantitative analysis. Correlation is used two describe the relationship between random variables and bivariate data. There are three co-relations—Positive, Negative, and Zero.

**What’s more – knowledge is not enough, hands-on skills are important **

The above are a handful of skills needed to work as a Data Scientist. They are required to understand more application-oriented skills – machine learning, applied mathematics, visualization, and predictive analysis. Learning these skills enable a Data Scientist to perform data analysis, where skills in statistics are needed, and predictive analysis where machine learning skills are needed, and so on. These are crucial skills to thrive in the data science industry**.**

However, learning these theoretically doesn’t help. Data science is much more application-oriented than any other role. Universities and colleges have come with short term courses, degrees, and best data science certifications which allow students to learn while putting these skills to use. Some certifications combine solving pressing industry problems using data

Additionally, enterprises, including Cloudera – Horton Works, SAS, etc. are increasingly realizing the demand for skilled Data Scientists, and are offering data science certifications. The data science industry is still maturing, and companies are still figuring out the best ways to build and manage data science processes. As companies increase their investments and companies increasingly put measures to avoid unnecessary losses, **certified data science professionals **are more in-demand.

Data security is a major concern among employers. Recent, data breaches, for instance, have made employers vulnerable to security threats. Thus, employers are reluctant to allow access to professionals who don’t have substantial experience. Experienced data science professionals hold the trust of employers when it comes to handling data.

**Progressing In A Data Science Career **

Learning should never stop. This is especially true in the case of data science. As data science evolves and solves some of the complex business problems. Aspirants and professionals will continually need to learn to upgrade their skills.