Tools used in science

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Start Your Coding Journey Now! Further, Data Scientists have to develop powerful predictive models using ML algorithms. All such functions cannot be accomplished without the help of such Data Science tools. So, if you wish to build a successful career in Data Science , you better start getting your hands dirty with these tools right away!

Data science is all about using large datasets and useful tools for extracting meaningful insights from a huge amount of data and turning them into actionable business insights. To make the work really easy, data scientists need to use some tools for better efficiency. Let us have a look at some of the most widely used data science tools: 1. SAS 2. Apache Spark 3. BigML 4. Excel Tableau 6. Jupyter 7. NLTK If you utilize these data science tools, you will find it pretty easy to develop actionable insights by analyzing the data.

Data Scientists find it easy to deal with a huge amount of structured as well as unstructured data by using the right tool. Different data scientists make use of different methods as per their requirements and convenience. Every method has its own importance and working efficiency. Yet, there are certain data science methods that are on the list of every data scientist for analyzing data and coming up with actionable insights from it. Some of the most widely used data science methods are: 1.

Regression 2. Clustering 3. Visualization 4. Decision Trees 5. Random Forests 6. Math is considered to be the foundation of Data Science. But, you don't need to worry because there is not so much math you need to learn to build your career in data science. If you Google up the math requirements for becoming a data scientist, you will constantly come across three concepts: calculus, statistics, and linear algebra.

But, let's get it clear that you need to learn a major portion of statistics for becoming a good data scientist. Linear algebra and calculus are considered to be a bit less important for data science. Other than that, one also needs to be clear with the fundamentals of discrete math, graph theory, and information theory for understanding and working efficiently with different data science methods and tools.

Data Science. Data Science All Courses M. Sc in Data Science — University of Arizona. The SlideShare family just got bigger. Home Explore Login Signup. Successfully reported this slideshow. We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime. Next SlideShares. You are reading a preview. Create your free account to continue reading. Sign Up. Upcoming SlideShare. Measurement and Measuring Length.

Embed Size px. Start on. Show related SlideShares at end. WordPress Shortcode. Share Email. Top clipped slide. Download Now Download Download to read offline. Science Tools Aug. Herman Seufert Follow. Measuring Length. Measuring length. Measuring length and time. Measurement tools. Measuring tools.