Start your career with these Data Science Skills.

As I am also a fresher in the field of Data Science and I’m learning Data science for the past year and after exploring a couple of courses and various articles I found the important skills which are required to start your journey as a Data Scientist.

1. Programming Skill: If we come in programming skill we must know at least one programming language from Python, R, or SAS. In 2018, 66% of data scientists reported using python every day and it overtook R as the most popular language for data science. Python is a multi-purpose, Object-Oriented programming language that is easy to deploy in applications or websites and comes with an active data science community, making it an easy choice for top tech companies.

Python:- With the knowledge of Python fundamentals, we will also want to explore Python libraries. The top Python libraries for data science include Pandas, NumPy, Matplotlib, Seaborn, SciPy, Sci-kit Learn, and Tensorflow.

R:- R is an open-source language used for statistical analysis, which has tools for presenting and communicating data-driven results. R programming may be more suited for research and academic work.

SAS:- SAS is a software suite with built-in Statistical functions and GUI (Graphical user interface) to guide fewer technical users. Science SAS is a very expensive enterprise software and Python and R are free to use, it makes sense to start with one of the other languages.

When deciding which language to use, you might want to consider the industry and company you are looking to enter.

SQL: SQL (Structured Query Language) is used for performing various operations on the data stored in the databases like updating records, deleting records, creating and modifying tables, views, etc. A Data Scientist can control, define, manipulate, create, and query the database using SQL commands.

Many modern industries have equipped their products data management with NoSQL technology but, SQL remains the ideal choice for many business intelligence tools and in-office operations.

Many of the Database platforms are modeled after SQL. Modern big data systems like Hadoop, Spark also make use of SQL only for maintaining the relational database systems and processing structured data.

2. Business Acumen: Data scientists are needed in nearly every industry. In order for data scientists to be effective, they should understand the field they are applying their skills to.

Business awareness could now be considered a prerequisite for effective data science. A data scientist should develop an understanding of the field they are working in before they are able to understand the meaning of data. This data makes up the industry's business intelligence, which is used to understand where the business is and the historical trends that have taken it there.

The unique goals, requirements, and limitations of each industry define every step that a data scientist takes. Without understanding the underlying aspects of the industry, it could be impossible to find meaningful insight or make useful recommendations.

A data scientist may be most effective when they truly understand the business they are advising.

3. Communication: The skills required of a data scientist can be sliced and diced in different ways. One important skill that every data scientist should have is communication. Data scientists act as a bridge between complex, uninterpretable raw data and actual people. Humans are inherently visual and can understand and process data better when it is presented visually.

4. Problem-Solving: A data scientist’s job is to understand how to take raw data and derive meaning from it. This requires more than just an understanding of advanced statistics and machine learning. They also need to integrate their understanding of the problem domain, available information, and their goals. Structured techniques such as Six Sigma are great tools to help data scientists solve real-world data science problems.

5. Data Wrangling and Data Preparation: As a data scientist, the ability to wrangle data ensures that we have good data going into our predictive models so that we can trust our results. Data scientists that are able to wrangle data benefit from the ability to prepare their own datasets, saving time and allowing for more time for model experimentation.

Common data problems including handling missing values and duplicate records, and applying the correct strategy to overcome these limitations can be the difference between a successful project and one that is plagued with error. Data wrangling is broad and includes examples such as data collection, complex SQL queries across multiple databases, and manipulation of data using Python.

6. Data Visualization: Data visualization could be an essential skill for all data scientists. Visualization plays two essential and equally important roles in data science. First, it enables the data scientist to see patterns and inform their exploration of the data. Second, it allows them to tell a compelling story using data. These are both essential parts of the data science workflow.

Scatter plots and histograms are essential elements of exploratory data analysis. Data storytelling requires a data scientist to creatively use data visualization to craft a narrative that informs the audience and explains their reasoning. Without these tools, data science could be ineffective at implementing change.

There are many data visualization tools available to data scientists: most programming languages provide libraries for visualizing data. Python data visualization can be done with Matplotlib and pandas. R offers ggplot2 as well as many other data visualization tools. Tableau and Power BI are high-level platforms for visualizing data from many different sources.

7. Statistics/Mathematics: Software runs all the necessary statistical tests these days, but a data scientist still needs to possess the statistical sensibility to know which test to run when and how to interpret the results. A solid understanding of multivariable calculus and linear algebra, which form the basis of many data analysis techniques, is likely to allow a data scientist to build in-house implementations of analysis routines as needed. Simply plotting data on a chart and understanding what it means are basic but essential first steps in the data science process.

Mathematical concepts such as logarithmic and exponential relationships are common in real-world data. Understanding and applying both the fundamentals as well as advanced statistical techniques allow data scientists to find meaning in data.

Though much of the mathematical heavy lifting is done by computers, understanding what makes this possible is essential.

8. Modeling and Machine Learning: Proficiency in predictive analytics is one of the essential data science skills when entering data science, and prospective data scientists should work to understand machine learning models, their use cases, and their limitations. Topics including knowledge of the benefits of specific models, ways to fine-tune model performance, and categorizing missing values are available.

Common machine learning models include the traditional statistical models such as linear or support vector machines (SVMs) to the most recent deep networks. As a result, data scientists should strive to continuously develop their predictive modeling abilities.

In addition to selecting the correct model to apply, data scientists must also master parameter tuning of machine learning models. Data science applicants that are knowledgeable of parameter tuning differentiate themselves from others by offering better performing models from the same source data.

Note: This article is for those who just started exploring data science or planning to start a data science career. The above article definitely helps you to start your career in Data Science.