How to become a data scientist? - kdnuggets
2020-11-06 01:20:06 【On jdon】
Traditional banking does not “ System ”, Because there is no data science system in these industries , Bank credit system based on massive data is the real system . Here is the original ：
From media to articles , Then to the post release , When it comes to the top leaders of large companies , The term that seems to be everywhere is “ Data Science ”, The hype is real . therefore , If you are familiar with technology and / Or interested in learning new knowledge about technology , Then you might think about the following questions ： What is data science ？ How one becomes a data scientist ？ ok , The answer will be given here .
Data science is an area , It involves using complex tools and algorithms , Machine learning process , mathematics , Statistics and other similar areas , Extract meaningful insights from raw data , Including usage , trend , Customer behavior, etc . Now , Data science is widely used in business . Companies are using data science to maximize profits , Build your own company to surpass others , Make their business cost-effective and make informed business decisions . therefore , Because data scientists always make wise and meaningful decisions when setting business goals , So it's lucky to adopt business data science .
The role and responsibilities of data scientists are enormous , And it's different from one area to another , So that people may be confused ： What exactly are data scientists doing ？ Is he a mathematician or a statistician or a computer engineer ？ Let's look at the roles and responsibilities of data scientists , Or the expectations of data scientists .
- Identify and identify problems based on data analysis , And see how they have a direct impact on customers and their needs .
- collect , Clean up and conversion , To deal with structured and unstructured data from many different sources .
- Looking for patterns in the data model , Identify opportunities and solutions for the company's growth , And solve the problems facing the company .
- The work of data scientists includes the ability to tell stories , This means that they should be able to explain their theories and concepts to stakeholders in a way that they can understand .
therefore , obviously , Data based decision making and advice to management teams is one of the main responsibilities of data scientists . Now? , To be a data scientist , You need to have some key skills . Because data scientists have a lot of responsibilities , So it's the same thing to qualify for the skills of a data scientist . We're going to talk about some of these .
The skills that data scientists need
data mining , Data analysis , computer programming , Statistics , machine learning , Data visualization , Big data analysis, etc , Are areas that contribute to the expertise of data scientists . To fulfill all the roles and responsibilities of a data scientist , The following is a vivid description of the skills .
1. mathematics （ Including statistics , probability , linear algebra ）：
Mathematics can be regarded as the core discipline of data science and technology . It's important for data scientists , Because when processing data and building data products , We need to look at the data and determine its texture and pattern mathematically . If you want to analyze and visualize the structured form of the transformed data , Must have good statistical knowledge . Linear algebra is also an integral part of learning data science , Because it is one of the important functions of machine learning , It's very helpful in revealing the characteristics of large datasets . therefore , in order to Learn about data science , People should be able to master these aspects of Mathematics .
2. computer programming ：
To prototype data models or repair complex data systems , Data scientist courses must include computer programming . The important programming languages and techniques commonly considered necessary for learning data science are Python,R,SAS,Perl,SQL And other recent and popular technologies . If you want to work in Data Science , So it's really necessary to have a deep understanding of any or all of these programming languages . There are many data science programs to help you learn all of these programming languages , And help you learn data science in the necessary way .
3. machine learning ：
Machine learning is to build or train a computer or system by continuously learning or developing itself by providing new data . From recommendation engines to self driving cars and other new technologies , Companies rely heavily on machine learning to improve the user experience . In short , Machine learning forms the core of artificial intelligence . Learn by machine , Companies can automate their systems , So it reduces the human workload , Time and energy , And make these systems cost-effective . Data scientist courses must include machine learning algorithms , Because they help make real-time decisions and high-value forecasts for the benefit of the company .
4. Data skills ：
One of the main responsibilities of data scientists is to always access , Storing and processing data . In order to have expertise in data processing ,SQL,MongoDB and Cassandra Knowledge of databases is very important . Next comes big data , Big data refers to a large amount of data generated at a large rate from multiple sources . Now? , These data cannot be processed by traditional database management systems such as relational databases . Big data can be achieved through Spark and Hadoop And other tools to solve the problem . These are open source software , Large data sets that can be used for distribution and processing .
5. Data collation and Visualization ：
Data processing is defined as the transformation of one data form to another . This is mainly done on raw data , Easy to understand and use . You can think of data visualization as a statistical graph , Graphs and information graphs to form and study visual representations of data science . In order to get meaningful datasets to improve the different departments and areas of the business , Introduces a process of arranging data into information reports , This is called data reporting .
therefore , You can find all the skills you need to learn data science and meet the data scientist position . The job of a data scientist as a career choice is an interesting choice , It's also very beneficial . however , Learning all of these skills alone is not enough to become a data scientist . Having expertise in all these skills is an important step towards data science professional qualifications . But there are several other steps that need to be taken , Only by combining all these steps together , To be qualified to work in data science .
Step by step guidance for scientists
Consider the fact that every day, globally, from desktops , Smart phones and a lot of IoT The amount of data generated by the device , Both the government and the private sector have to rely on data scientists to process and process data . Manipulating the data . In terms of career choice , Choosing a career in data science is a relatively new direction . however , That doesn't mean it's impossible . When data scientists perform data analysis , They usually do this to build predictive models using machine learning and deep learning protocols . The responsibility of a data scientist also includes determining which model is best suited to which data to analyze . Since all models are approximate representations of the current or future society , They need to be fine tuned , So we need to rely on the mathematical expertise of data scientists . therefore , There are many things to consider and consider their importance . therefore , Here are the steps to become a data scientist .
1. Prepare from an early stage ：
Preparation is always the basic step to achieve the goal , It's better than getting ready early , in other words , Even before you go to any university to study or hire a data scientist , It's better to prepare than that . Course ？ As mentioned earlier , Make yourself proficient in Java,Python,R A widely used programming language is a good starting point . Again , Learning the basics of statistics and mathematics may also be good for you .
2. With a bachelor's degree ：
With computer science , statistical , information technology , Mathematics and even data science （ If there is ） An undergraduate degree in any subject will help you . This is because , The most eligible students for jobs in data science come from these disciplines , So that even a minor in any of these subjects will benefit from it . In addition to studying your undergraduate degree , Looking for internship opportunities , And ask your predecessors or professors to help you get the opportunity to study data science or help you to engage in a career in Data Science , It would be wise of you , Because you will gain more experience, the better for you .
3. Get entry-level data science work ：
Large companies are often looking for entry-level jobs in data science that come with students or new students to fill in the gaps . therefore , Junior data scientist and junior data analyst positions are required for data science work . Now? , For these jobs , Data science courses may help , Because there is no better way to learn data science than to use data science courses . Good learning is a good institution for this , Because excellent data science courses are not only efficient , And it can effectively make it easy for you to do these entry-level jobs .
4. Get a master's or doctor's degree ：
Having a master's or doctor's degree will be very helpful , Because in the data science profession , Employed by people the company is looking for , Access to higher education is much better .
5. A promotion ：
These extra degrees and higher education , Plus experience , Can really promote your data science career , So that you can get a much-needed Promotion . Combine technical skills with leadership quality , Will pave the way for better opportunities . Learning all the skills mentioned is the key factor , These are just additional conditions for scientists to get such high demand for data .
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