Data Scientist Skills That Will Give You an Advantage

Possessing these technical skills will provide you with an edge over your peers:

• Statistics (e.g., hypothesis testing and summary statistics)
• Math (e.g., linear algebra, calculus, and probability)
• Machine learning tools and techniques (e.g., k-nearest neighbors, random forests, ensemble methods, etc.)
• Data mining
• Software engineering skills (e.g., distributed computing, algorithms and data structures)
• Data visualization (e.g., ggplot and d3.js) and reporting techniques
• Data cleaning and munging
• R or SAS languages
• Unstructured data techniques
• Python (most common), C/C++ Java, Perl
• SQL databases and database querying languages
• Big data platforms like Hadoop, Hive & Pig

Course Content

• Introduction to Data Science
• Understanding Exploratory Data Analysis
• Machine Learning
• Model Selection and evaluation
• Data Warehousing
• Data Mining
• Data visualization
• Cloud computing
• Business Intelligence
• Data Storytelling
• Communication and Presentation

Data Scientist Learning Path

A person looking to be a well-rounded senior data scientist can follow the recommended learning path shown below. 

SAS

SAS is a computer programming language that is used for statistical analysis. It stands as the undisputed market leader in the commercial analytics space.

SAS updates are developed in a controlled environment and are thus always well tested compared to open source. The language is easy to learn and provides a simple option for professionals who already have an established knowledge of SQL.

Many businesses distrust freeware and don’t like the idea of not having a software provider verify the efficacy of their application usage. Then there is the matter of market opinion – SAS is leading the advanced analytics segment with a 31.6 percent market share, according to IDC.

R

With the R certification and training, professionals will be competent in R programming language concepts such as data visualizations, exploration, and statistical concepts like linear and logistic regression, cluster analysis, and forecasting.

R is open-source, has a vibrant community, has libraries for extensive analytics and visualization, has a steep learning curve, and integrates with big data and Hadoop. And compared to other languages, R still stands as the one that produces a higher salary of $115,531. It is one of the most in-demand skills.

Data scientists and statisticians around the world use this programming language to solve some of their most challenging problems in fields that range from computational biology to quantitative marketing.

Since complex data is represented through charts and graphs, the language has become an essential part of the data analysis process.

Hadoop

An open-source framework, Hadoop is used for distributed processing and distributed storage of large data sets.

Hadoop is written in Java; all of the modules are devised with the central assumption that hardware failures are ordinary and common and should be handled automatically by the software.

Hadoop has opened new doors for data scientists to store and process data. Instead of depending on proprietary hardware and other systems to process and store data, Hadoop allows parallel distributed processing of massive amounts of data across industry-standard servers that will process and store data. With Hadoop, no data is too big.

For more information on these programming languages, or any other programming languages that are important to a data scientist, feel free to download the eBook, ‘Top Programming Languages for a Data Scientist.’

Our fee structure is most reasonable as per current market demands. We understand the importance of hard earned money and hence we have very cost effective fee structure for different role based trainings which is affordable compared to other institute in market.

We also have discount plans in case of more students from single college or group of students willing to enroll for specific course. In group of students we provide special discount making the total fee even more affordable.

• Live online Classes
• Jr Coding Classes
• Role Based Training
• Programming/Coding and Frameworks
• Full Stack Development
• Internship (Online/Offline) 
• Live Project Training

What is Data Science?

Data Science combines statistics, maths, specialised programs, artificial intelligence, machine learning etc. Data Science is simply the application of specific principles and analytic techniques to extract information from data used in strategic planning, decision making, etc. Simply, data science means analysing data for actionable insights.

Is Data Science Hard To Learn?

No. Anyone with the desire and commitment can learn data science. There are plenty of resources for beginners, and there are also courses and bootcamps where you can study data science. The math you’ll need as a beginner is quite foundational. 

Is Data Science a Good Career?

Yes. There is a huge demand for data scientists in various industries, and salaries have also grown commensurately. Data science can also give you the opportunity to contribute to your company in meaningful ways. 

What Is Logistic Regression?

Logistic regression is a form of predictive analysis. It is used to find the relationships that exist between a dependent binary variable and one or more independent variables by employing a logistic regression equation. 

What Is a Decision Tree?

Decision trees are a tool used to classify data and determine the possibility of defined outcomes in a system. The base of the tree is known as the root node. The root node branches out into decision nodes based on the various decisions that can be made at each stage. Decision nodes flow into lead nodes, which represent the consequence of each decision.