There has been a continuous rise in Data Science and Artificial Intelligence in the past half-decade with the demand for skilled Data Scientists and AI Engineers. Although these are entire careers in themselves, it doesn’t limit their reach, as a data scientist can make an impact at any level in just about any organization in the world. The Database, Statistics in AI, and Advance Mathematics Plays Important role in Career Path of Data Science and Artificial Intelligence.

But what exactly is the most important thing that you should have coupled with extensive knowledge to get successful in these Career Paths?, Answer is Skills. There are certain skills specific to the fields of data science as well as Artificial Intelligence. Luckily, we have created a comprehensive list of the most essential skills for you.

**Databasesâ€“**

Databases are probably the most critical aspect of both Data Science and Artificial Intelligence since these fields draw their entire power and features from numerous datasets and handling them efficiently is thus an important skill to master.

Having a thorough knowledge of Structured Query Language (SQL) becomes a necessity when it comes to handling databases. Although NoSQL and Hadoop have become a large component of data science, it is still expected from a candidate to have proper knowledge of SQL.

Databases are handled with the help of a Database Management System (DBMS) which is capable of editing and manipulating a database. In larger systems, a DBMS assists users in storing and retrieving as well as define certain rules for validation of data at any given point of time.

Thus, database handling is an important skill because if you cannot handle big chunks of data, how would you leverage them in Data Science & AI.

**Statisticsâ€“**

A sound understanding of statistics is vital for a data scientist. It is the key that allows you to collect, organize, analyze, and interpret data. Statistics play an important role in every level of an organization, from technical to Business level.

It is especially crucial for data-driven companies such as Google, Facebook, YouTube, etc, where the profit of the stakeholders depends on statistical data and AI programs that run on these datasets to them in making data-driven decisions.

Statistical theorems aid data scientists to understand the capabilities, but also the limitations and assumptions of these data-driven techniques.

Some of the topics of statistics that are important in data science and AI are Mean, Median, Mode, Standard Deviation, Correlation, Bayes Theorem, A/B Testing, etc. Probability and statistics are interwoven together since statistical methods along with the theory of probability allow experts to make estimates for further analysis.

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**Advance Mathematics â€“**

The entire field of Data Science and Artificial Intelligence is based on Mathematics and this makes the knowledge of advanced mathematics especially multivariable calculus a necessity for anyone in this field.

Multi-variable calculus is essential for building machine learning models as it consists of several predictors and unknown variables. Several mathematical concepts like logarithmic and exponential relationships are common in real-world data and it is through the application of advanced mathematical formulas only, that data scientists can find meaning in this data.

Linear Algebra is also an important topic here since most of the data-sets are represented as matrices. Although computing is done with the help of computers but still the formulae and the understanding of concepts such as irrational and rational numbers help data scientists in writing efficient and accurate code.

Some of the concepts that are useful here are Derivatives and gradients, Scalar, vector, matrix and tensor functions, Function plotting, Step function, Sigmoid function, Logit function, etc.

**Python/R â€“**

Knowing Python/R programming is similar to having a car if you want to drive. AI and Data Science essentially are about programming and it is the programming knowledge that brings together all the fundamental skills needed to transform raw data into actionable insights. Python and R are the most preferred languages when it comes to data science although companies might be satisfied with the knowledge of only one of them.

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**Python:** Python, unlike R, was not developed for data science but its openness and easy-to-learn nature along with the fact that it is a high level, powerful, and friendly programming language, has made it popular among experts.

Some of the Python libraries that are essential and popular in this field are:

- Basic Libraries: NumPy, SciPy, Pandas, Ipython, matplotlib
- Libraries for Machine Learning: sci-kit-learn, Theano, TensorFlow
- Libraries for Data Mining & Natural Language Processing: Scrapy, NLTK, Pattern

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**R:** R is an open-source language and has a long history of usage in statistics and data analytics, although there was a time when it was only confined to academia.

It provides extensive resources and packages for data analysis such as:

- Tidyverse
- Dplyr
- Ggplot2
- Caret
- Stringr

Thus, Python and R languages act as oars in the boat of Data Science and AI.

**C++/Java â€“**

Although Java and C++ might not be the first choice when it comes to data science, still these languages have an irreplaceable place in a data scientist’s toolkit.

There is no denying the fact that there are a large number of tools and frameworks available in the market that are better functioning but it is also a fact that almost 90% of the systems around the world rely on Java or C/C++ at one level or another.

Also, many companies don’t actually implement their data science algorithms using R/Python, instead, they re-build the same algorithm inside a piece of software that is built-in something like C++ or Java. Thus. most of the Data Science jobs require familiarity with C++/Java.

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**Algorithms â€“**

Data is of no use unless we know a way to analyze it and convert it into a more functioning form. Algorithms are the essence of Data Science and AI and one of the major tasks of a data science expert includes the creation and maintenance of effective and efficient algorithms.

Data Structures and Algorithms are like the roots of the data science tree and having a solid background in these is equally important, since a fruitful algorithm might result in huge wins for your organization.

Machine learning which is a sub-branch of Data science helps in the creation of numerous predictive models and if a person is aware of the proper machine learning algorithms, all that one needs to do is perform certain adjustments to this algorithm and make it work your way.

It is because of the importance of algorithms, that many students choose to master data structures and algorithms before heading into data science and AI.

In summary, weâ€™ve discussed the essential skills required for practicing data scientists. Data science and AI are two fields that are interdependent and ever-evolving. Mastering the foundations of these fields will provide you with the necessary background to pursue advanced concepts. So, get going with your journey since now is the perfect time to start.

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