Python data science cookbook over 60 practical recipes to help you explore Python and its robust data science capabilities BOOK PDF FULL FREE 2022.

Python data science cookbook  over 60 practical recipes to help you explore Python and its robust data science capabilities.

Data Science Training: The Preparation You Need

Why do you think Facebook can automatically tag people's faces? 

Why can websites like Netflix provide video recommendations with a certain percentage of matches? 

And why can banks identify which customers are likely to be loyal and which are not? In this article, you can find a summary explanation of data science, which you can now learn through data science training without even going to college. 

Read this article for the full review! What is Data Science? Broadly speaking, you can summarize data science as a combination of mathematics, business acumen, tools, algorithms, and machine learning techniques, all of which help us find certain insights or patterns hidden in raw data to be used in the business decision-making process. In data science, you will come across structured and unstructured data. The algorithms involved here make use of predictive analysis applied to the data. Therefore, data science covers not only the present, but also the future.

 As such, data science discovers trends based on historical data that can benefit current decisions and find patterns that can be modeled and used for future predictions. 

 Why Join Data Science Training to Learn? In today's digital era, the amount of data generated is overwhelming, including data used in analytics. 

That's why data science is now a necessity for businesses, regardless of industry. 

 In order to make the most of data, companies from all sectors 

- finance, retail marketing, banks, and IT 

- need data scientists. This has led to an increase in industry demand for data scientists around the world, which in turn has affected the salaries offered for this profession. In fact, IBM has labeled the data scientist profession as the trending job of the 21st century. 

 By looking at the high industry demand for this profession, coupled with the large opportunities to work in the world's top companies, it is certainly not surprising that many are studying data science. 

In fact, you can also pursue a career as a data scientist without having to go through a formal path or IT background by taking data science training, as many people have proven. 

 Read also: Career Paths and Job Prospects for Data Scientists Data Science Skills You Need To become a reliable data scientist, there are a series of skills that you need to fulfill. These various skills can be summarized into 3 main points below:

Tools In-depth knowledge of R:

R is used for data analysis, as a programming language, in statistical analysis, and data visualization. Coding with Python Until now, Python is a popular mathematical counselor and model among data professionals, including data scientists. You see, Python has an abundance of packages or libraries to build and deploy data models. Microsoft Excel In data science, Ms. Excel has become a basic requirement for entry-level positions. This software is equipped with various features to help the process of analyzing data, applying formulas, equations, and diagrams from large amounts of data. Hadoop This platform is an open source distributed processing framework that is used to manage the processing and storage of big data applications. Database or SQL coding SQL is used primarily for dataset preparation and retrieval. Not only that, SQL can also be used for problems related to graph and network analysis, search behavior, fraud detection, and so on. Technology Since there is so much unstructured data out there, you will need to know how to access it. There are many ways you can do this, such as through APIs or through web servers. Also read: What are the Responsibilities and Job Desk of a Data Analyst? Engineering Mathematical skills Not only technological literacy, a data scientist also needs to have mathematical skills. Especially when you will be struggling with machine learning algorithms such as regression, clustering, time series, and so on which require intensive mathematical knowledge and expertise. Plus, these algorithms are based on mathematical algorithms. Ability to work with unstructured data Given that data is produced every day in various forms - images, comments, tweets, search histories, and so on - and is unstructured, it will benefit you greatly if you can "transform" them into structured data for your next work. Business understanding This skill is most important for data scientists who are already in middle or upper management positions in the company hierarchy. However, you don't need to worry because you can train your business acumen and understanding from the start. This means you don't need to be a business or management major to master this skill. Application of Data Science in the Real Industry Still hesitant to join data science training because you're not sure how this field is applied in any industry? As mentioned earlier, data science is needed in almost every industry. This means that you can be sure that companies whose products or services you often see or even use every day must also have a data science function in it. Some examples are as follows: Health industry Here, data science is used to build sophisticated medical instruments to detect and cure diseases. Gaming industry Various video and PC games today have been created with the intervention of data science. As a result, today's gaming experience is much more sophisticated and even on a different level from the previous era. Image recognition In this industry, there are several examples of the use of data science that are very common in everyday life. For example, to help identify patterns in images, as well as detect objects in images Recommendation system in streaming services If you're a subscriber to streaming services such as Netflix, HBO Go, or Disney+ Hotstar, you'll be aware that the app often provides viewing recommendations automatically. These recommendations are made with the role of data science as well so that they are not just randomly selected. Fraud detection system This function is most commonly found in the banking and finance industry. Here, data science and related algorithms are used to detect suspicious transactions.

Learn Data Science from 0 with Digital Skola​:

Are you convinced to learn to be a data scientist? If so, Digital Skola has the right data science training solution for you. Through the Data Science Bootcamp program, you can learn to become a reliable and work-ready data scientist in just 3 months, even without having an IT background! Python is one of the most popular programming languages in the world. TIOBE's index shows that Python's popularity ranks third in the world, behind C and Java. Python is becoming more and more popular as the popularity of data science increases. It has become the most common programming language used in data science work. In this article, we will discuss the use of Python in data science, and to begin with let's start with the basic question, 'What is Python?'

What is Python?

Python is an interpreted, object oriented and high level programming language. Conceptually, Python is similar to other programming languages like Java, C++, R, and so on. Historically, Python was created by Guido van Rossum in 1990 who promoted it as an open source programming language. Since then, Python has become one of the most important programming languages in the world with a large community. Python's popularity continues to rise with the increasing popularity of programming-related professions including data science. Why Python? There are several reasons why Python is a popular and important programming language in the world. First, Python is open source, which means that everyone can use it for free. Second, Python is the perfect tool for a wide range of programmers and developers. The Python interface can be used with a series of functions that can develop various algorithms. Third, Python packages are complete and useful for data science. Fourth, it uses shorter script codes than other programming languages. Fifth, Python's syntax is simple and intuitive because it is basically English. Sixth, it uses relatively fewer keywords. Seventh, having a large community makes it easier to collaborate. Use of Python in Data Science Data collection & cleaning With Python, a data scientist can use almost any type of data available in various formats such as CSV (Comma-separated value), TSV (tab-separated valueI), or JSON. Python also allows importing tables directly from SQL to code scrapping websites. Data Exploration After having clean data, a data scientist must find business questions to answer which are then converted to data science questions. In this process, a data scientist needs Python to identify their properties and separate the data based on its type such as numeric, ordinal, nominal, or categorical to prepare the treatment needed. Data Visualization & Interpretation Python has many data visualization packages. Matplotlib is the library most often used by data scientists to generate basic graphs and charts. To create aesthetic data visualizations, data scientists can use another Python library, Plotly. Data Modeling This phase is a very crucial phase in the data science work process. In this phase, a data scientist must strive to minimize the dimensionality of the dataset. In this process, data scientists can use Python which has many advanced libraries that can help the machine learning process to carry out commands related to data modeling. Deploying The deployment process is the process of transforming the model into a meaningful language that can be understood by the system and end users. One of the most commonly used frameworks in this process is Flask. Python Data Types There are several known Python data types including Text Type (str), Numeric Type (int, float, complex), Sequence Type (list, tuple, ranges), Mapping Type (dict), Set Type (set, frozenset), Boolean Type (bool), and Binary types (bytes, bytearray, memoryview).

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