Today, thanks to the digital age we have the ability to accumulate massive quantities of data that require non-traditional methods for processing data and software.
How to Become a Data Scientist?
Data Science is the area of study that focuses on extracting information from the data collected. There's a high need for experts who can translate data analysis into an advantage for their businesses. If you are Data Scientist, you'll be able to create analytical and business solutions based on data.
Data Science at Work
Have you heard that the media services company Netflix employs data science in a large way? Netflix measures its the level of engagement and retention of its users, for example:
- If you stop, you can rewind or fast-forward
- What is the day of the week? the time of the day you view video
- Why and when you quit the content
- The place in the world that you're viewing from
- Your browsing and scrolling habits
- What is the device you use to watch
Netflix has more than 120 million people worldwide! To process all this data, Netflix uses advanced data science metrics. This allows it to show an improved movie and offer recommendations to its viewers as well as create better content for users. The Netflix popular show House of Cards was developed with the help of data science and large data. Netflix took information from viewers of the show West Wing, another drama which takes place at the White House. It took into account the point at which people stopped watching were fast-forwarding and also when they ended their viewing. By analyzing this data, it allowed Netflix to design what it believed to be an exceptionally engaging and enjoyable show.
Now let's explore some of the crucial skills of a data scientist that a person should have.
7 Skills To Become A Data Scientist
To be Data Scientist, you'll have to acquire competencies that include the following:
- Skills 1: Develop understanding of databases that will allow you to analyze and store data using tools like Oracle(r) Database, MySQL(r), Microsoft(r) SQL Server, and Teradata(r).
- Skills 2: Understand mathematics, probability and statistics analyses. Statistics is the science of developing and studying methods of collecting, analyzing, and interpreting and presenting data from empirical sources. Probability is the measurement of the probability that an event will happen.
Mathematical analysis is a branch of mathematics which deals with limitations and related theories such as differentiation and integration, measurement infinite series, as well as analytic functions.
- Skills 3: Be able to master at minimum the basics of one language for programming. Programming tools such as R, Python, and SAS are essential for performing data analysis.
R is a no-cost software application for graphic and statistical computation that supports the majority of Machine Learning algorithms for Data Analytics like regression or association as well as clustering.
Python is an open source, general-purpose programming language. Python libraries such as NumPy along with SciPy are employed for Data Science.
SAS can extract, modify and manage data from various sources and also perform statistical analysis of the data.
- Ability 4 Get Data Wrangling that includes cleaning, manipulating and arranging data. The most popular instruments for data manipulation are R, Python, Flume and Scoop.
- Skill 5: Learn the basics that comprise Machine Learning. Systems that are able to automatically learn and grow through experience, without having to be specifically programed to. Machine Learning can be accomplished with various methods, including regressions Naive Bayes, SVM, K Means Clustering KNN, KNN, and Decision Tree algorithms to name some.
- Skills 6 having the ability to work with Big Data tools such as Apache Spark, Hadoop, Talend and Tableau that are utilized to handle massive and complicated data that isn't possible to handle with traditional software for data processing.
- Skills 7 Learn to visually display the results. Data visualization involves integrating various data sets and creating visual displays of the data using charts, diagrams and graphs.
Careers in Data Science
When you've learned these abilities, you'll have many opportunities for career advancement that are available. Get ready for your job interview by using our questions for interviews in data science.
Average salary: $120,931
The data scientists develop business solutions based on data and analytics that drive improvements and optimization of the development of products. They employ predictive models to enhance and improve customer experience and revenue generation, as well as advertising, and many other. Data scientists also collaborate with functional teams across the organization to develop models and track the outcomes.
Average salary: $137,776
Data engineers assemble large, complex data sets. They determine, design and implement internal process improvements , and then construct the infrastructure necessary to optimize extraction, transformation, and loading. They also develop analytical tools that use this data flow.
Average salary: $112,764
Data architects look at the structural requirements of new applications and software, and create database solutions. They set up and configure systems for information and move data from existing system into new ones.
Average salary: $65,470
Analysts in data gather information from either primary or secondary sources and manage databases. They interpret the data, analyse the results using statistical techniques and create data collection systems, as well as other tools that assist management in prioritizing requirements for information and business.
Average salary: $70,170
Business analysts help companies in planning and monitoring by gathering and organizing requirements. They evaluate the needs of resources and create cost estimate models by providing clear, actionable, and repeatable reports.
Average salary: $54,364
Data administrators help database development and maintenance of existing databases. They are accountable in establishing and testing new databases as well as data processing systems. They are also responsible for maintaining the integrity and security of databases as well as creating complicated queries that permit data to be taken out.
Preparing for a Career in Data Science
The SynergisticIT's Data Science course is an integrated course that combines AI as well as data science. It includes the following rigorous courses that will help you prepare for a thrilling job in the field of data science
- Data Science with Python
- Machine Learning
- Deep Learning
- Computer Vision
The process of mastering in the area of data science starts by understanding and using the fundamental technology frameworks that are that are used to analyze big data. Learn the development as well as programming tools Hadoop and Spark that process huge quantities of data in a distributed computing environment and gain expertise in the complex data science algorithms as well as their implementation with R which is the most popular language for processing statistical data. The insights you get from data are then displayed in consumable reports that are based on tools for data visualization, such as Tableau.
After you've mastered the management of data and predictive analytic methods, you'll gain exposure to the latest machine learning technology. This extensive data science education pathway will allow you to excel throughout the full spectrum of data science and big data techniques and methods.
SynergisticIT's Data Science course is extensive, and obtaining an award is proof that you've made the first step in understanding the field. The skills and knowledge that you acquire through working on projects and simulations as well as studying the case studies will help you get over the other students.