Data Science Tutorials: A Beginner's Guide
Table of Content
The Data Science lesson is intended for persons of various skill levels who are interested in becoming Data Scientists, including novices who are students, professionals, or tech enthusiasts. Data Science is at the centre of this session. This tutorial will instruct you on the fundamental concepts, abilities, tools, and definition of data science that you should be aware of.
WHAT AND WHY
The world, at large, has become a global village of sorts with the rise of the internet. This proliferation of internet space has also caused a rise in the increase in available data. This rise in the amount of data available came with an initial problem which was storage. The drive of technologically inclined companies, then, was to be able to build something that would be perfect storage for a large amount of this data. Over time, this issue of storage has been resolved but it begs the question, what are we to do with so much data? This is where Data Science comes in.
Prior to today, the main software programmes utilised to cater to information and their analysis were business intelligence products. But there has recently been somewhat of an information explosion. This amount of data is not presented in compact or organised ways. Instead, you have a tonne of raw data from sources like social media feeds, blogs, security cameras, sensors, and multimedia files, among others.
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In these forms, there is hardly anything that can be done on these data with Business Intelligence tools. With Data Science, there are better tools able to handle huge amounts of data from all kinds of sources and use them to solve problems and carry out innovations.
DATA SCIENCE: A HISTORY
More partnerships resulted from this thesis, and eventually the International Association for Statistical Computing (IASC) was founded. This was the first significant development, fusing the concepts of computers and statistics with the intention of gathering data and turning it into information. Another work by Turkey, titled Exploratory Data Analysis, was published in 1977. In the essay, he makes the argument that both confirmatory and exploratory data analysis should be compatible with one another.
The inaugural workshop for the ACM SIGKDD Conference on Knowledge Discovery and Data Mining took place in 1989. This gathered authors and thinkers from several disciplines together in one location for discussions and practicals on the potential of data science.
This encouraged more people in this generation to consider fresh approaches to problems. Jacob Zahavi made the case in 1999 that new mechanisms were required to regulate the volume of data that firms might now access. He writes in his article Mining for Nuggets of Knowledge that traditional statistical techniques perform well with tiny data sets. With millions of rows and dozens of columns of data in today's databases, it is a technological challenge to create models that can better analyse the data. In order to address website decisions, specialised data mining tools may need to be created. More significant developments in the subject of data science occurred by 2001. Software-as-a-Service (SaaS) emerged as a precursor to operations and applications that use the cloud. In a similar spirit, William S. Cleveland started a training programme titled Data Science: An Action Plan for Expanding the Technical Areas of the Field of Statistics the same year, complete with an action plan. It intended to increase technical proficiency and include research on data use. By 2002, The Data Science Journal has published the study's findings in a journal. The magazine covered a wide range of subjects in its articles, including data infrastructure and systems, how to use that data, and the legal ramifications of data science. In order to solve the storage issue, Hadoop 0.1.0 was introduced in 2006, and by 2008, more and more individuals were embracing the title of "Data Scientist" or using it in conversation. It is frequently attributed to Patil and Hammerbacher of LinkedIn and Facebook for the increase in usage at the time.
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In this analysis, the year 2011 had the greatest increase in employment opportunities for data scientists. The number of programmes and seminars geared towards comprehending big data expanded by exactly 15000%, increasing job chances for people in this industry. The idea of a data lake instead of a data warehouse was also put out in the same year. Thanks to IBM data, it was discovered in 2013 that more than 90% of all information was nonexistent two years prior. There was more information now.
Deep learning algorithms enabled Google Assistant's voice and speech recognition to improve by roughly 49% by 2015. In a piece for Bloomberg, Jack Clark noted that artificial intelligence (AI) had a banner year. Over the course of the year, the overall number of software projects utilising AI surged to over 2,700 projects.
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The biggest boon in this study took place in 2011 when jobs were specifically for Data Scientists. Job opportunities for professionals in this field increased by exactly 15000%, an increase in programs and seminars targeted at understanding big data. It was also in that same year that the concept of Data Lake was proposed, as opposed to Data Warehouse. By 2013, thanks to IBM statistics, it had been noted that over 90% of all the information existent were nonexistent two years before. There was now more data.
By 2015, with the help of deep learning techniques, Google Assistant's voice’s speech recognition and voice moved up by about 49 percent. Bloomberg’s, Jack Clark, also wrote that it had been a year filled with milestones for Artificial Intelligence (AI). The total of software projects using AI skyrocketed to more than 2,700 projects over the year.
DATA SCIENCE: THE FUTURE
According to Kenneth Cukier in a write-up published in The Economist (2010), Data Scientists are able to fuse the abilities of a programmer, artist, and statistician to bring out the value within raw volumes of confusing data. Since its inception, Data Science has managed to grow into an even bigger field and saturate a large number of other fields of study which need data to function. Which field doesn't? Thus, Data Scientists are needed now more than ever in different fields of endeavour.
Nevertheless, there are professionals in the field who believe that the quality and quantity of good data scientists capable of handling the kind of data existent now are at an all-time low. For example, Half Varian, a Google Chief Economist stated that the lack of such sets of people with these skills could lead to a future where these skills would be even of more value as the rate of data existence increases.
EXAMPLES OF JOBS THAT DATA SCIENTISTS CAN DO
There are several work prospects in the field of data science. Data Scientist, Data and Analytics Manager, Data Analyst, Data Engineer, Machine Learning Engineer, and Statistician are just a few positions that data scientists are adept at handling.
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COMPONENTS OF DATA SCIENCE
There are building blocks that make up this field of study called Data Science. In order to better understand Data Science, one must first be acquainted with these. For example:
- Data Engineering: This focuses on the data's structure and how it may be obtained, saved, utilised, or modified sufficiently to include metadata in addition to the data.
- Statistics: In a field where the major thing of value is data, understanding it is of great importance. In order to understand data, one needs a good understanding of statistics, how data interact with each other to make meaning. It is a major component in Data Science.
- Mathematics: These topics serve as one of the main pillars of data science. One is better able to comprehend and interpret the quality, amount, value, structure, and development of data using mathematics. One could find it simpler to handle and comprehend data in Data Science with the aid of mathematics.
- Machine Learning: It is through Machine Learning that models are built. It is also the means by which AI and other machinery get to interact with data, amongst others.
- Visualisation: Data is viewed and comprehended in context through visualisation. A data scientist would be stuck attempting to comprehend something in the dark without it.
- Advanced Computing: Computers are one of the major things that Data Scientists interact with. Therefore, it is an added advantage to any Data Scientist to be able to understand advanced computing and its applications in design, bug features, and writing.
- Domain Expertise: Due to its involvement in several domains, this component provides the in-depth explanations required in various fields.
Data Science Tools
Data Science tools can be broadly divided into four main parts within which they are used, namely: Data storage, data visualization, data modeling, and data analysis.
- Data Storage: As the name implies, these tools are used to store large amounts of data. Examples include Hadoop, Apache Spark, and Microsoft HD Insights, amongst others.
- Data Visualization: These tools allow you to visualize your data and understand the insights it has been able to find. Examples of such include Tableau, Seaborn, and Matplotlib, to mention but a few.
- Data Modeling: These help you with your days and the complex algorithms within them. Some tools in this category are BigML, Scikit Learn, and Tensorflow.
- Data Analysis: Also called Exploratory Data Analysis (EDA) are tools built to analyze large volumes of raw data. Examples of such tools are SAS, Python, Informatica, and MATLAB.
The many tools used in data science are explained in depth in this article.
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Data Science: Methodology
We would go into the techniques used in data science to comprehend data and find solutions in this section of the lesson.
- Problem to Approach
- Requirements to Collection
- Understanding to Preparation
- Modelling to Evaluation
- Deployment to Feedback
PROBLEM TO APPROACH METHODOLOGY
Here, the objective is to understand the issue through the information at hand and then utilise that information to identify a solution.
REQUIREMENT TO COLLECTION METHODOLOGY
Since the problem is known, the focus is on what the problem requires for it to be solved. The data requirements are then listed and once this is done, the data scientist goes to pick (collect) from the available data to solve the problem.
UNDERSTANDING TO PREPARATION METHODOLOGY
The majority of the necessary data have already been gathered, and it is now being evaluated and reported using descriptive statistics and mathematical operations like mean, median, and mode. After that, the data is cleaned up and checked for errors such as erroneous or missing data before being prepared for modelling. To prevent having to start over, this step is vital and should be properly completed.
MODELLING TO EVALUATION METHODOLOGY
At this stage, the focus is on building models that would either be descriptive or predictive with the existing data. Descriptive models help especially in recommendation while predictive models excel at telling the foreseeable future based on existing data. Thereafter, the models are then evaluated and tested to make sure it is solving the issues it was created for. Evaluation is used to do an appraisal of the model.
DEPLOYMENT TO FEEDBACK METHODOLOGY
The model is delivered for final testing when it has been evaluated and the data scientist is satisfied with it. Following this deployment, customers provide input on whether they think the work was done well or where it might have been done better.
DATA SCIENCE: ADVANTAGES
- Increased Demand: The rise of the Internet and technology is creating a larger influx of data. Thus, Data Scientists are in high demand. Different fields from healthcare to banking and finance, to SMEs, amongst others, are making more use of data and are in need of professionals in this field.
- Better Healthcare: Machine Learning and other Data Science components are making healthcare more precise and capable in diagnosis and the best steps to take. The struggle against viral outbreaks, pandemics, and cancer, could have remedies in the future with the help of Data Science.
- Improved Customer Experience: With the help of Data Science, product recommendations are more precise and customized to the whims and likes of clients.
DATA SCIENCE: DISADVANTAGES
- Overreliance on Data: Although Data Science makes for more data, it cannot guarantee such data. This implies that data may be employed despite being unverified and provide unexpected consequences.
- Privacy Issues: The problem of privacy poses a significant concern to any businesses using data. Despite being valuable for businesses, data may contain information that a customer wants to remain private. The security of this sensitive information is more in jeopardy due to the rising allegations of breaches and online deception.
DATA SCIENCE: APPLICATIONS
- Prediction: It takes data from places like ships, satellites, aircraft, buoys, and land stations to predict weather conditions and imminent natural disasters such as storms, earthquakes, and floods, to mention but a few. The idea is to make use of these data to know when to expect some of these things which could have far-reaching effects on the business and its profitability, diminishing the amount of risk and damage that would have been incurred.
- Product Recommendation: Conventional business methods provided suggestions based on information from browsers and customer purchase histories. However, via the use of data science, more information collected from a wider range of sources, such as social media, likes, comments, and frequently visited websites, may provide more accurate product suggestions. This greatly enhances and maximises the consumer experience.
- Control: Furthermore, it offers a larger perspective through which Artificial Intelligence devices, without human input, can make more informed decisions and choices that would be as close to the choices a human would take or the most logical decision to make at the time. A quintessential example can be seen in AI cars, where data is obtained from sensors; maps; surveillance cameras, and traffic areas.
Speech to text and forecasting are two other uses and advantages.
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