Among many careers in the industry, there is a tremendous demand for data scientists in the industry. It falls among the most remarkable jobs in America and pays well, especially for individuals with the right skills. You must know that transitioning to data science has never been easy. However, if you have done computer science, you can easily transition to a data scientist, unlike people in the varying sector. Most people want to pursue data science because it has many job opportunities in the industry currently.
In order to become a data sicentists within nine months, a person should teach themselves to code in Python and R. The student should also acquaint themselves with Tableau, the Hadoop framework, and Apache Spark. Some data science certifications can be acquired quickly through Coursera.
Furthermore, it takes someone some good time to become a data scientist. And everyone admires being a data scientist because almost all company currently is a technical company. Therefore, if you already have a professional and you want to take another step into data science, worry no more since this article has you covered. Here is an overview of some tips you would need to become a data scientist in nine months.
Coding/ SQL Database
SQL helps you enhances your profile and understand a better rational database as a professional data scientist. You must be able to write as well as execute SQL (structured query language) complex queries. However, Hadoop and NoSQL have emerged as the most significant component in the entire data science. Bear in mind that SQL is a programming language that can help you carry out functions, such as extracting data from the database, delete, and add. With SQL, you can also be able to transform the database structures and execute analytical functions. Besides, as a data scientist, you need to be skilled in SQL. Coding/ SQL Database helps you to access, work on your data, and communicate. On the other hand, it minimizes the programming duration you might require to handle more complex queries.
Education
To be a data scientist, you need to have sufficient education. Statistics indicated that 46% of data scientists have PhDs, while 88% have Master’s degrees. This means you need to have a high and robust educational background to acquire the correct knowledge and skills to become a data scientist. Moreover, if you become a data scientist, you can get a Bachelor’s degree in statistics, physical sciences, social science, and computer science. Similarly, the most popular field of studies that suit data science includes Engineering, Computer Science, Mathematics and statistics. If you have a degree in these courses, you probably have the appropriate knowledge and skills needed to analyze and process massive data.
As mentioned before, most data scientists have a Master’s degree while others have PhDs, which means after accomplishing your degree program to better your course. If you upgrade your studies to a Master’s degree, it is recommended that you choose courses such as Astrophysics, Mathematics, Data Science, among many other related field courses. With these courses, you will experience easy transitioning to data science. After that, you can enroll in online classes to acquire unique skills, like Big Data Querying or Hadoop. Ensure that you put into practice what you have been learning. You can even decide to create an app, exploring data analysis, or even commencing data analysis to help you better your skills.
Roadmap
Before you start your journey of becoming a data scientist, you need to develop a plan. It can quite be challenging for working professionals to complete such a broad course in nine months. If you are not careful, you might end up spending numerous years to make ends meet. To avoid all these hassles, ensure that you plan how you will make this course successful in 9 months. First, you must begin by assessing yourself on what you know and what you are not aware of. Through this, you will be able to understand what mandatory skills you need to acquire.
Take your time and try to find out what suits you best. You can achieve this by examining industries leveraging data science. Make sure that you also find out how many jobs data science offers and understand the job description. Suppose you are looking forward to pursuing data science in nine months; you need a roadmap to your destination. Furthermore, this will help you know how to start the learning process. Good planning will help you stay on track since you will focus on what you want in data science.
Make good use of free content
As much as you might have paid for your online course, it would help if you kept perusing free resources, which are mostly found online. Multiple channels can make you knowledgeable about data science, the solution to hackathons, and programming, but YouTube is the best online platform. YouTube videos are free as long as you have access to the internet. Besides, they also guarantee you crucial knowledge of data science.
However, you might come across some videos that might not give you in-depth information about data science but offer you knowledge worth it. Therefore, you can always watch free videos on data science on your YouTube channel whenever you are not practicing, studying, or working. Data science needs you to push yourself to the limit. Thus, ensure you make good maximum use of free content.
Hadoop platform
A data scientist might encounter a scenario where the data volume outshines the system’s memory or is expected to send data to varying servers. All these can be achievable through the use of the Hadoop platform. The use of Hadoop makes the conveying of data to numerous points of your system easier. On the other hand, you can also use it for summarization, data sampling, data filtration, and data exploration.
This does not fall under data science requirements, but in most cases, it is preferred. Bear in mind that having sufficient experience with pig and hive can also be a robust selling point for you. It would help if you also made yourself familiar with cloud tools, such as Amazon S3. Statistic carried out on 3490 LinkedIn data science jobs by CrowdFlower indicates that the Hadoop platform is a crucial skill for data scientists with a rating of 49%.
Networking
Most people wonder how they can network, yet they have a time frame of only nine months to study data science. You need to understand that networking is also a process of learning. Moreover, you do not have to consume most of your precious time connecting and meeting with individuals, especially from the data science community. However, you can make a point of attending conferences and events based on data science.
Additionally, this fuels your learning process since you will know how the industry functions through data conferences and events. Likewise, you will familiarize yourself with the entire job roles and how different companies do their hiring. All this information is on the internet, but it becomes more exciting and transparent through interaction and in-person talks. Networking can make someone step in and help you make learning success by assisting you in your projects and generating you with materials and resources.
R programming
This falls among the analytical tools, and for a data scientist, R can be the order of the day because it meets data science requirements. Therefore, you can comfortably use R to solve any given issue you are likely to experience in data science. 43% of data scientists usually use R to solve statistical problems, but it features a steep learning curve. If you have mastered the programming language, you might find it challenging to learn R programming. Even though it might be challenging, there is always a way out since vast resources, especially on the internet, help you get familiar with R programming. You can opt for Simplilearn’s Data Science Training with the R Programming Language. Furthermore, R programming is an excellent resource for aspiring data scientists.
Understand Data
Suppose you do not have context data can be misleading because data requires you to have a story to narrate a story. There is always an association of data with its ways, methods, context, and surroundings. It involves the entire life cycle starting from where it was initiated, produced, utilized, modified, executed, and terminated. While conversing, all data scientists use technologies, such as buzzwords, Tableau, NoSQL, and Hadoop. With data, you must create an intimate relationship with it. This means you must 100% familiarize yourself with it. Therefore, before learning to become a data scientist, ensure that you clearly understand data.
Data visualization
A lot of data is usually generated frequently in the business domain. In addition, to understand the data quickly, you need to translate it into a format. In real-life situations, people understand images more in graphs and charts, unlike raw data. A picture gives you more information than any amount of words.
Data scientists visualize your entire data via data visualization equipment, like Tableau, Matplottlib, d3.js, and ggplot. These tools are essential because they have the maximum potential to convert your project results that are complex to a format that you can easily understand.
Bear in mind that multiple individuals barely understand P values or even serial correlation. This means you have the task of ensuring that you show them what all those terminologies represent visually. Similarly, data visualization allows organizations or companies to directly work with your data, allowing them to easily get an insight, which will help them act and initiate modern business opportunities. You must know that data visualization helps you to become competent in the industry.
Python coding
Python coding is the famous coding language used in data science roles. It is used alongside C/C++, Perl, or even Java; data scientists value it because it is a remarkable programming language. You must know that O’Reilly surveyed 40% of respondents, and they turned out to be using python as their primary programming language. Python coding is versatile, and you can comfortably use it in your entire data science processes. It handles many data formats, and you can easily import SQL tables into the code. Besides, it also allows you generate datasets, which can help get ant given dataset type you require on google.
Teamwork
To become a data scientist in nine months, you cannot afford to work alone; you must apply teamwork. It is essential to work with the organization’s executives to enhance strategies. On the other hand, you will work with the designers and product managers to develop excellent products. You will find yourself working with everyone in the company to deliver maximum performance. Moreover, you will work together with your entire team members to generate use cases, enabling you to know the business goal and the exact data you require to solve issues. However, you need to find the correct approach and tackle the use cases and the data necessary to solve the issue. You also need to understand how to present and translate the result into a format, which can be easily understood. Solidarity is the key to becoming a data scientist in nine months. Choose solidarity forever.
Practice
If you want to become an expert in data science, you must make a routine of practicing what your study, which must take a reasonable duration. With this, you will keep the knowledge and skills you have gathered flowing. Ensure that you are not overloaded with work to make sufficient time for your practice session. To become successful, especially in nine months, you must be ready to take a unique approach. You can confidently practice once you reach home or take time out at your workstation; all these can work for you.
If you have grasped sufficient knowledge and you have an idea of how to go about it, then your office can be the ideal place to commence practicing. You can even ask your colleagues who pursued data science to help you in programming, which can be successful when both of you are not occupied. Ensure that you utilize your breaks efficiently since it cannot take you a whole one hour to have your lunch.
Likewise, you can choose to practice after work, but you can be limited somehow because it will be colliding with your family time. When practicing from home, ensure that you choose excellent data science projects and programming to acquire maximum experience. In addition, you can also engage in hackathons to obtain a genuine experience. With proper practicing, you can become a professional scientist in only nine months because practice makes perfect.
Communication skills
In the current industry, organizations are in search of robust data scientists. They need a data scientist who can fluently and translate their entire technical findings to a non-technical team, like the sales and marketing department. Therefore, your communication skill will make you emerge the best within a very short duration. As a data scientist, you must arm the organization with quantified insights and understand the demands of the non-technical workmates to come up with the correct data. Apart from speaking the same language, it would help if you also communicated via data storytelling.
Thus, a data scientist must know how to create the storyline around their data to make it easier for everyone to comprehend. Furthermore, storytelling helps you to communicate your entire findings to your employers correctly. Whenever you are communicating, it is better to ensure that you emphasize the values and the results you found in your data analysis. You must also understand that most company owners usually have no interest in your analysis, but they are interested in how much it can positively impact their business. Ensure that you learn and focus on creating lasting relationships as well as delivering remarkable value via communication. Communication is essential for a data scientist since it helps people easily understand what you are putting across.
Apache Spark
This a vast data computation framework, and it is similar to Hadoop. Besides, it falls among the famous big data technology worldwide, but they vary because the spark functions faster. Similarly, Hadoop writes and reads to disk, and the process is too slow, while the spark captures its computation in the memory. Bear in mind that apache spark is essential in data science because it aids in running its complicated algorithms faster. Additionally, it assists in data dissemination processing, especially when working on a lot of data, hence saving your time. This also helps you to tackle complex unstructured data sets as a data scientist because you can comfortably use them on many machines or a single machine. With apache-spark, a data scientist can avoid data loss in data science. The apache-spark speed strength is determined by its platform, which makes data science projects easy to carry out.
Conclusion
With this comprehensive post, you are now familiar with the tips that can make you become a data scientist within nine months. Therefore, ensure that you implement the above information to become a successful data scientist and meet the organization’s demands. With the advancement in technology and initiation of technical companies’ job opportunities are everywhere for data scientists. Besides, data scientists have zero disappointments and deliver incredible performance after their nine months of studies.