This Data Science A-Z™: Real-Life Data Science Exercises Included course gives you the most comprehensive boot camp to learn the invaluable skills and techniques used by experts in data analytics, data mining, statistical modelling, data visualization, Tableau, and data science. We’ll learn about the how the data scientist works with data effortlessly and how you start an incredible career in data science. So, are you interested in learning the best-selling data science course named Data Science A-Z™: Real-Life Data Science Exercises Included? At Udemy, you’ll get a very affordable price using coupon.
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Introduction to Udemy Data Science Course
Learning the Data Science A-Z™: Real-Life Data Science Exercises Included course, no matter what your skill levels are, you can pick up the techniques you need to know to be a good data scientist. If you’re ready for the self-improvement, this course will give you more ideas for: performing data mining in Tableau, preparing data for analysis, performing data visualization, modelling and curving-fit your data, and working like an expert in data science.
Once joined the Data Science A-Z™: Real-Life Data Science Exercises Included course, you’ll get 21 hours of on-demand video, 4 articles, and 213 lectures in total. And to learn the course only requires learners have a strong willing to get the most out of it, no prior experience of data analysis is assumed. This course will teach you all the fundamentals of data science, and the advanced skills to be qualified in starting an awesome career in data science.
So, whether you’re a student, developer, or data analyst, this Data Science A-Z™: Real-Life Data Science Exercises Included course will be a good choice for mastering all the data mining skills, statistical modelling skills, data preparation skills, and data science skills.
What each lecture will teach you:
- There are 40 lectures on the introduction to the part 1, including the knowledge of visualization, Tableau tool, data mining and advanced data mining skills and techniques
- There are 63 lectures will teach the part 2 knowledge, including everything you need to know about modelling
- There are 83 lectures focus on introducing the part 3 knowledge that you need to know all about the data preparation
- The last 28 lectures will tell you continue the part 4 knowledge. You’ll learn how to work with people, present for data scientist, and more skills needed in communication.
What is Data Science?
By learning the Data Science A-Z™: Real-Life Data Science Exercises Included course, you’ll get a deep learning of data science. Data Science is the study of all the tools related to the extraction of useful information from a huge amount of data. It involves various disciplines like:
- Machine learning
- Data engineering
- Pattern matching
- Data visualization
All these come together under one head to process the given data for research and marketing purposes. For instance, you are offered cheaper car insurance if you have a good credit score. Why? Simply because the data scientists at the insurance companies found that people having a good credit score are highly responsible and are less likely to get involved in a car accident. When the goal is to find the best customers for minimizing liability, the insurance company looks for the set of people that most likely would not make use of their insurance.
The process of data science begins at the idea of achieving a goal. Several steps that follow the idea.
- It starts with a question in mind. There is a set business goal or research conclusion to be reached.
- It is then followed by deciding how much and what type of data would be needed to arrive at a conclusion. And then comes the question of how to gather that information.
- When the data is collected, it is reviewed for any falsifications, irrelevance, repetition or misunderstood information.
- The algorithms are then written down before processing the data.
- Data models are then built which undergo further statistical and recursive analysis. The results are compared against other techniques to ascertain reliability.
- The conclusion is then derived and results are formulated.
The whole science related to data is an interesting approach for studying the trends and finding hidden information. It answers the open-ended questions about the patterns existing in the world around us. It is an exploratory way to discover future outcomes by analyzing the current or past decisions. To be a good data scientist, the Data Science A-Z™: Real-Life Data Science Exercises Included will tell you the secrets.
In this Data Science A-Z™: Real-Life Data Science Exercises Included course, you’ll learn all about the data mining. Simply put, data mining is a process where large amounts of data are analyzed to find hidden information, patterns and behavior of a group. Mining involves algorithms and research on huge databases that results in information that can be used for various other purposes. The process is also known as KDD, or Knowledge Discovery in Data.
The science behind data mining asks for two things- huge data and high computing power. Multiple algorithms need to be designed to discover a single pattern from heaps of data. Supercomputers and computing clusters are essential to mine petabytes of data.
Talking about the technical uses of data mining, there are largely four main featured ones:
- Pattern predictions based on analysis of trend and behavior.
- Predictions based on likely outcomes.
- Using decision oriented information to create strategies.
- Discovering previously unknown facts by studying documented facts.
For example, google analyzes the search queries by millions of people around the world and displays the most commonly used snippets when you start typing. This is the reason why you do not have to type the whole sentence into the search engine most of the times. Another example can be the credit card companies. On studying the shopping patterns of individuals, they can design suitable promotional offers dedicated to areas where people spend the most money.
There are many type of data mining in the industry today. Some of the famous ones are:
- Pattern Recognition
- Bayesian Network – Deriving conclusions based on connected or influencing attributes.
- Neural Network- A model that predicts correctly but is highly technical and difficult to interpret.
- Classification Tree- Combining all the attributes and deriving conclusions based on previously accumulated data.
The process comes handy in research and marketing areas of any business. It is all about finding new information hidden in a lot of data.
Statistical Modelling Skills
Josh Wills said that a data scientist is a person who is better at statistics than any programmer and better at programming than any statistician. Being a good data scientist needs that you be in love with numbers and the excitement of finding the hidden patterns behind them. This Data Science A-Z™: Real-Life Data Science Exercises Included course will tell you the secrets to be a good scientist. The core of organizing and analyzing data comes from statistical modelling.
As is quite obvious, statistical modelling emerged as a sub-field of statistics. Today, data is driving most businesses and markets around the world. Stats models enable the data experts to analyze and visualize petabytes of data and derive a concluding pattern out of it. Statistical learning is focused on developing and interpreting these models, while also studying their precision and uncertainty.
For example, car insurance cost varies across cities depending upon the figures of road accidents in a particular area. An employee having statistical modelling skills can easily develop a model to feed the data of car accidents into and arrive at the most profitable marketing strategy for the company.
Statistics is a fundamental ingredient in the process of becoming a modern data scientist. You need to know where it all comes from and how it develops into complex programs. A masters in statistical modelling skills adds high value to your CV. You become a highly sought after candidate by the companies.
A person who knows the basic of data processing can develop strategies for all types of fields and requirements. Designing algorithms is the 80% work while coding. All areas of work like banking, finance, automobile, insurance and even food retail businesses need statisticians today. The profits of most companies rely on marketing strategies and expansions. Till now, are you interested in learning more about the statistical modelling skills? The Data Science A-Z™: Real-Life Data Science Exercises Included course is highly recommended.
Tableau is a data visualization tool used in the field of data analytics and business intelligence. The drag and drop feature enables a user to easily access and analyze data. The software helps in creating innovative reports and visualizations and that can be interestingly shared around later. Its compatibility with Excel, SQL, and cloud-based data repositories makes it an ultimate choice for all data scientists. In this Data Science A-Z™: Real-Life Data Science Exercises Included course, you’ll learn how to work with the popular Tableau tool effortlessly.
You can work on dashboards, worksheets and stories in the workspace of the software. There are five featured products of Tableau which can be chosen based on the user’s type of work, namely:
- Public (massive and public non-commercial Tableau server)
- Desktop (locally creating dashboards and stories)
- Server (connecting to data sources and sharing work products)
- Online (creating dashboards and stories on the Cloud)
- Reader (viewing dashboards and worksheets locally)
It offers robust solutions for organizing, structuring and presenting the results received from a specific set of data with great ease.
Some of the main highlights that make Tableau a successful user-friendly platform are-
- Data Connectors
Redshift, Google Analytics, Cloudera Hadoop, SQL Server, Salesforce, MongoDB, PDF files, Dropbox, Amazon Athena and spatial files are some of the notable data sources that Tableau can connect to, both live and in-memory without any programming.
- Easy Data Switch
Tableau allows to easily switch between the extracted data and other live data connections. Simply put, the live and in-memory data can be handled smoothly.
- Platform Flexibility
The device designer tool helps the user to design, customize, and publish dashboards on various platforms like mobiles, tablets, and desktops. You can make your results perfectly compatible with the device you wish to stream them on.
- Dashboard Embedding
The dashboards can be embedded into existing applications like Salesforce and Jive. You can perform quick analysis where you need them most.
- Drag and Drop
This feature holds the main key of ease of use. It makes integrating data and creating visuals far simpler. So, join the Data Science A-Z™: Real-Life Data Science Exercises Included today, get a depth learning of the Tableau like an expert.
Why Master Data Science?
The focus of technological advancement had been on increasing the processing power and pushing possibilities. But today, it has shifted to software- driven applications. Data is being called as the “oil of the digital economy“. Machine learning, artificial intelligence and statistics drive the innovation industry today. Data science is all about collecting data and finding the hidden patterns inside. Studying user behavior worldwide helps to design compatible and profitable business models.
Mastering Data Science opens up a plethora of career opportunities for you. Data and information has become important to all businesses at all levels. Data scientists are needed for strategic decision making processes by studying related numbers and patterns. Be it banking, finance, automobile, insurance, energy, healthcare, transport, retail, e-commerce, fashion, architecture and any other domain that you can think about- data experts are needed in abundance today.
Fields like Big Data and Data Analytics are gaining mass popularity with highly paid job offers. A masters in Data Science makes you a highly desirable candidate by any company. Research has found that more than half of the job listings for the post of data scientists have postgraduate qualification as a prerequisite. University placements have seen a surge for data science students.
Some common job titles for Data science postgraduates are analytics engineer, big data engineer, data architect, data engineer, data visualization specialist, business intelligence architect and business analyst. In this Data Science A-Z™: Real-Life Data Science Exercises Included course, you’ll clearly understand why it’s worthy of learning data science.
The demand for data specialists is not limited to a country or region. Every business around the world uses analysis and customer patterns to design its marketing strategy and product cost. The blueprint itself revolves around the behavior of their targeted customers.
A master in Data Science can land you an assured job with handsome salary and benefits. The continuous developments in the field is likely to keep you engaged and hungry to learn more.
So, time to learn all about data science and data analytics? Don’t miss out on learning the best-selling Data Science A-Z™: Real-Life Data Science Exercises Included course at Udemy.
Refer to the relative course here: Machine Learning A-Z™: Hands-On Python & R In Data Science
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