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There are several data scientist positions, but few individuals have the data science skills necessary to perform these increasingly essential roles.
Data Science for Dummies is the exact starting point for IT professionals and students who want a quick briefing on all areas of the expansive data science space.
Focusing on business cases, the post explores topics in big data, data science, and data engineering, and how these three areas are combined to produce remarkable value.
If you really desire to pick-up the skills you need to begin a new career or initiate a new project, reading this post will help you understand what technologies, programming languages, and mathematical methods on which to focus.
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While this post on data science for dummies functions as a wildly fantastic guide, sometimes intimidating field of big data and data science, it is not an instruction manual for hands-on implementation. Here’s what to expect:
* Provides a background in big data and data engineering before moving on to data science and how it’s applied to generate value
* Includes coverage of big data frameworks like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL
* Explains machine learning and many of its algorithms as well as artificial intelligence and the evolution of the Internet of Things
* Details data visualization techniques that can be used to showcase, summarize, and communicate the data insights you generate It’s a big, big data world out there let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.
How do you Explain Data Science for Dummies
Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data.
What is data science in layman’s terms?
Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data — both structured and unstructured.
Is data science a good career?
Data science for dummies is one of the most highly paid jobs.
According to Glassdoor, Data scientists make an average of $116,100 per year. This makes Data Science for dummies a highly lucrative career option
Jobs in data science abound, but few people have the data science skills needed to fill these progressively important roles.
What are the basics to learn data science?
Technical Skills: Computer Science
- Python Coding. Python is the most common coding language I typically see required in data science roles, along with Java, Perl, or C/C++. …
- Hadoop Platform. …
- SQL Database/Coding. …
- Apache Spark. …
- Machine Learning and AI. …
- Data Visualization. …
- Unstructured data.
Seeing What You Need to Know When Getting Started in Data Science
Traditionally, big data is the term for data that has incredible volume, velocity, and variety. Traditional database technologies aren’t capable of handling big data — more innovative data-engineered solutions are required.
To evaluate your project for whether it qualifies as a big data project, consider the following criteria:
- Volume: Between 1 terabyte/year and10 petabytes/year
- Velocity: Between 30 kilobytes/second and 30 gigabytes/second
- Variety: Combined sources of unstructured, semi-structured, and structured data
Data science and data engineering are not the same
Hiring managers tend to confuse the roles of data scientists and data engineers. While it is possible to find someone who does a little of both, each field is incredibly complex. It’s unlikely that you’ll find someone with robust skills and experience in both areas.
For this reason, it’s important to be able to identify what type of specialist is most appropriate for helping you achieve your specific goals. The descriptions below should help you do that.
- Data scientists: Data scientists use coding, quantitative methods (mathematical, statistical, and machine learning), and highly specialized expertise in their study area to derive solutions to complex business and scientific problems.
- Data engineers: Data engineers use skills in computer science and software engineering to design systems for, and solve problems with, handling and manipulating big data sets.
Data science and business intelligence are also not the same
Business-centric data scientists and business analysts who do business intelligence are like cousins. Both types of specialists use data to achieve the same business goals, but their approaches, technologies, and functions are different. The descriptions below spell out the differences between the two roles.
- Business intelligence (BI): BI solutions are generally built using datasets generated internally — from within an organization rather than from without, in other words. Common tools and technologies include online analytical processing, extract transform and load, and data warehousing. Although BI sometimes involves forward-looking methods like forecasting, these methods are based on simple mathematical inferences from historical or current data.
- Business-centric data science: Business-centric data science solutions are built using datasets that are both internal and external to an organization. Common tools, technologies, and skillsets include cloud-based analytics platforms, statistical and mathematical programming, machine learning, and data analysis using Python and R, and advanced data visualization. Business-centric data scientists use advanced mathematical or statistical methods to analyze and generate predictions from vast amounts of business data.
Looking at the Basics of Statistics, Machine Learning, and Mathematical Methods in Data Science
If statistics have been described as the science of deriving insights from data, then what’s the difference between a statistician and a data scientist? Good question! While many tasks in data science require a fair bit of statistical know-how, the scope and breadth of a data scientist’s knowledge and skill base is distinct from those of a statistician. The core distinctions are outlined below.
- Subject matter expertise: One of the core features of data scientists is that they offer a sophisticated degree of expertise in the area to which they apply their analytical methods. Data scientists need this so that they’re able to truly understand the implications and applications of the data insights they generate. A data scientist should have enough subject matter expertise to be able to identify the significance of their findings and independently decide how to proceed in the analysis.
In contrast, statisticians usually have an incredibly deep knowledge of statistics, but very little expertise in the subject matters to which they apply statistical methods. Most of the time, statisticians are required to consult with external subject matter experts to truly get a firm grasp on the significance of their findings and to be able to decide the best way to move forward in an analysis.
- Mathematical and machine learning approaches: Statisticians rely mostly on statistical methods and processes when deriving insights from data. In contrast, data scientists are required to pull from a wide variety of techniques to derive data insights. These include statistical methods, but also include approaches that are not based in statistics — like those found in mathematics, clustering, classification, and non-statistical machine learning approaches.
Looking at your coding toolset
Choosing the Best Programming Languages for Data Science
Coding is one of the primary skills in a data scientist’s toolbox. Some incredibly powerful applications have successfully done away with the need to code in some data-science contexts, but you’re never going to be able to use those applications for custom analysis and visualization.
For advanced tasks, you’re going to have to code things up for yourself, using either the Python programming language or the R programming language.
Using Python for data science
Python is an easy-to-learn, human-readable programming language that you can use for advanced data munging, analysis, and visualization. You can install it and set it up incredibly easily, and you can more easily learn Python than the R programming language. Python runs on Mac, Windows, and UNIX.
IPython for data science offers a very user-friendly coding interface for people who don’t like coding from the command line.
If you download and install the Anaconda Python distribution, you get your IPython/Jupyter environment, as well as NumPy, SciPy, MatPlotLib, Pandas, and sci-kit-learn libraries (among others) that you’ll likely need in your data sense-making procedures.
The base NumPy package is the basic facilitator for scientific computing in Python. It provides containers/array structures that you can use to do computations with both vectors and matrices (like in R).
SciPy and Pandas are the Python libraries that are most commonly used for scientific and technical computing.
They offer tons of mathematical algorithms that are simply not available in other Python libraries.
Popular functionalities include linear algebra, matrix math, sparse matrix functionalities, statistics, and data munging. MatPlotLib is Python’s premier data visualization library.
Lastly, the sci-kit-learn library is useful for machine learning, data pre-processing, and model evaluation.
Using R for data science
R is another popular programming language that’s used for statistical and scientific computing. Writing analysis and visualization routines in R is known as R scripting.
R has been specifically developed for statistical computing, and consequently, it has a more plentiful offering of open-source statistical computing packages than Python’s offerings.
Also, R’s data visualizations capabilities are somewhat more sophisticated than Python’s, and generally easier to generate. That being said, as a language, Python is a fair bit easier for beginners to learn.
Using Visualization Techniques to Communicate Data Science Insights
All of the information and insight in the world is useless if it can’t be communicated. If data scientists cannot clearly communicate their findings to others, potentially valuable data insights may remain unexploited.
- Know thy audience: Since data visualizations are designed for a whole spectrum of different audiences, different purposes, and different skill levels, the first step to designing a great data visualization is to know your audience. Since each audience will be comprised of a unique class of consumers, each with their unique data visualization needs, it’s essential to clarify exactly for whom you’re designing.
- Choose appropriate design styles: After considering your audience, choosing the most appropriate design style is also critical. If your goal is to entice your audience into taking a deeper, more analytical dive into the visualization, then use a design style that induces a calculating and exacting response in its viewers. If you want your data visualization to fuel your audience’s passion, use an emotionally compelling design style instead.
- Choose smart data graphic types: Lastly, make sure to pick graphic types that dramatically display the data trends you’re seeking to reveal. You can display the same data trend in many ways, but some methods deliver a visual message more effectively than others. Pick the graphic type that most directly delivers a clear, comprehensive visual message.
Going with analytics dashboards
When the word “dashboard” comes up, many people associate it with old-fashioned business intelligence solutions. This association is faulty. A dashboard is just another way of using visualization methods to communicate data insights.
Leveraging Geographic Information Systems (GIS) software
Geographic information systems (GIS) is another understated resource in data science. When you need to discover and quantify location-based trends in your dataset, GIS is the perfect solution for the job.
Maps are one form of spatial data visualization that you can generate using GIS, but GIS software is also good for more advanced forms of analysis and visualization. The two most popular GIS solutions are detailed below.
- ArcGIS for Desktop: Proprietary ArcGIS for Desktop is the most widely used map-making application.
- QGIS: If you don’t have the money to invest in ArcGIS for Desktop, you can use open-source QGIS to accomplish most of the same goals for free.
For data visualization, you can use the ggplot2 package, which has all the standard data graphic types, plus a lot more.
Lastly, R’s network analysis packages are pretty special as well. For example, you can use graph and StatNet for social network analysis, genetic mapping, traffic planning, and even hydraulic modeling.