The job of a data scientist is much in demand in the 21st century as it is termed the “hottest job.” It is an interesting job and requires a good knowledge of programming and analytical skills. So, what is this job all about? Who is a data scientist? Let’s know it.
Who is a Data Scientist?
A data scientist is an analytical data expert who optimizes the growth of the organization by analyzing the business from its huge volume of data, cleaning it, and then running certain AI and ML algorithms on the refined data sets to provide enhanced outcomes that prove to be beneficial for the growth of the organization.
Data Science Process
There are certain steps that a data scientist follows to reach the ultimate goal of better decision making and thus providing strategic business moves. The stages are as follow:
- Business Problem Understanding: Data scientists meet with the clients and discuss end goals and decide targets. The data scientist must communicate well to understand the requirements and thus should be curious and ask as many questions as possible.
- Acquire Data: The next step is to collect data from numerous sources like webservers, logs, and APIs.
- Data Preparation: After data collection comes data preparation, which involves data cleaning and data transformation. Data cleaning is a time-consuming process and involves inconsistent data types, misspelt attributes, missing and duplicate values, and so on. Later, data is transformed based on defined mapping rules.
- Exploratory Data Analysis: This step refines and defines the selection of features and variables that will be used in developing the model. This is a crucial step in the complete data science process.
- Data Modeling: This is the core activity, which uses diverse ML techniques like KNN, decision trees, and Naive Bayes to identify the model that best fits the business requirements.
- Visualization: In this stage, the scientist meets the client again to communicate the business findings in a simple manner. For this stage, the data scientist uses data visualization tools.
- Deploy and Maintain: Test the model in a pre-production environment and then deploy it in the production environment(s). Then scientists use reports and dashboards to get real-time analytics and also monitors and maintains the performance of the data model.
Skill Sets Required to Become a Data Scientist
- Programming Skills: Being a data scientist requires you to be fluent in languages like Python, R, and Scala. Knowledge of other programming languages like C, C++, and Java is also helpful. Python is a versatile programming language for all the steps of the data science process. It can take any format of data, and SQL tables could also be uploaded easily.
- Databases and Frameworks: They contribute massively to handle huge volumes of data. Databases like SQL and frameworks like Apache Spark and Hadoop are very much in demand in the data science industry.
- Mathematics and Statistics: Mathematics is required to process and structure the massive data that data scientists deal with. The data scientist must be good at linear algebra, calculus, and statistics. Statistics allows to play with data and eventually extract the insights to predict reasonable outcomes. A data scientist is expected to know how to use statistics to infer insights from smaller data sets that are applicable to larger populations.
- Data Analysis: It becomes easy to contemplate data with data analysis and thus gives deeper and significant insights. Because of analysis, the market can be studied thoroughly and thus; it leads to effective marketing actions.
- Data Intuition: Companies expect you to be a data-driven problem-solver.
- Machine Learning: There is a collection of machine learning algorithms to make predictions based on the data set fed.
- Natural Language Processing(NLP)
- Algorithms: Gain expertise in the following algorithms:
- Linear Regression
- Logistic Regression
- Decision Tree
- Random Forest
- K Nearest Neighbor
- Clustering (for instance, K-means)
- Business Acumen: Data scientists not only work and analyze big amounts of data but also understand the intricacies of business organizations.
Tools that Aspiring Data Scientists Must Learn
Big Data Frameworks and Tools
- HDFS (Hadoop Distributed File System): It is the storage part of Hadoop.
- Yarn: Performs resource management by allocating resources to different applications and scheduling jobs.
- MapReduce: It is a parallel processing paradigm that allows data to be processed parallelly on top of HDFS.
- Hive: Mainly used for creating reports, this tool caters to the professional form of SQL background to perform analytics on top of HDFS.
- Apache Pig: This high-level platform is for data transformation. It works on top of Hadoop.
- Scoop Flu: It is a tool used to import unstructured data from HDFS and import and export structured data from a DBMS.
- Zookeeper: It acts as a coordinator among the distributed services running in a Hadoop environment, thus helping to configure management and synchronize services.
- Suze: It is a scheduler that binds multiple logical jobs together and helps to accomplish a complete task.
Real-Time Processing Frameworks
- Apache Spark: This distributed real-time framework is used in the industry rigorously. It offers smooth integration with Hadoop and leveraging HDFS as well.
DBMS and Database Architectures
A database management system stores, organizes and manages a large amount of information within a single software application. Thus, this helps to manage data efficiently and allows users to perform multiple tasks with ease. It also improves data sharing, data security, and data access and offers better data integration with minimizing data inconsistencies.
- SQL-based Technologies: SQL helps to structure, manipulate and manage data stored in relational databases. Therefore, a strong understanding is required of at least one of the SQL-based technologies listed below:
- IBM DB2
- SQL SERVER
- Postgre SQL
- NoSQL Technologies: As the requirement of the organizations has grown beyond structured data, NoSQL technology has seen an increase in the adoption rate. It can store a massive amount of unstructured, semi-structured, or structured data with quick hydration and adjoin structure as per application requirements. Some of the prominently used NoSQL databases are:
- HBASE: It is a column-oriented database that is great for scalable and distributed big data stores.
- Cassandra: This is a highly scalable database with incremental scalability. The best feature of this tool is minimal administration and no single point of failure. Further, it is good for applications with fast and random reads and writes.
- MongoDB: This is a document-oriented NoSQL database. It gives full index support for high performance and replication for fault tolerance. It has a () master/slave architecture and is rigorously used by web applications and for semi-structured data handling.
Programming and Scripting Languages
Various programming language serves the same purpose, so mastering one of the following is a must:
- Python: Highly recommended to learn.
- R: Developed by statisticians, this language has a steep learning curve and is generally used by analysts.
Data warehousing is crucial when the data is fed into heterogeneous sources. As such, we need to apply ETL operations. Data warehousing is used for analytics and reporting. This is important for business intelligence solutions. Following are the popular ETL tools:
- Talend: The major benefit of this tool is the support for big data frameworks.
- Qlik Q
All mathematical tools are based on one of the following operating systems:
Career Path to Become a Data Scientist
- Be Qualified: Many job descriptions mention the candidate to have a Master’s or Ph.D. in Computer Science, Mathematics, Statistics, or Engineering. The other way to become eligible for the role of a data scientist is by learning online. As such, various e-learning platforms have become a reasonable and efficient way to learn specialist data science skills, and that too at an affordable price.
- Develop Technical Skills: Apart from the tools stated above, a candidate is also expected to have hands-on experience with some AI and machine learning tools and algorithms. Also, visualizing and presenting data with software or platforms such as ggplot, d3.js, and Tableau is a plus.
- Non-Technical Skills: Apart from technical skills, a data scientist must possess the following non-technical abilities:
- Attention to detail
- Organizational skills
- Desire to learn
- Resilience and focus
- Communication and teamwork
- Build Your Portfolio: It is important to make an impressive first impression. Therefore, make a good quality resume. An even better option is to put up a website to demonstrate your work and experience.
- Build Network: Go to conferences and meetups to get exposure and stay updated with your field. Although there are many such conferences and meetups, the ones that are highly popular are listed below:
- The Strata Data Conference
- Knowledge Discovery in Data Mining (KDD)
- Neural Information Processing Systems (NeurlPS)
- The International Conference on Machine Learning(ICML)
- SF Data Mining
- Data Science DC
- Data Science London
- Bay Area R User Group
- Ace the Interview: There are a number of sites and blogs to help you with this. The major requirement is that you must have a good knowledge of algorithms.
By now, you will be able to successfully answer the questions, “Who is a Data Scientist?” and “How to Become a Data Scientist?” There is no doubt that data scientist is one of the most lucrative career options of the 21st century. Of course, you need to have a wide skill set and the will to face and overcome challenges.
As the world is heading towards an age where huge loads of data is commonplace, the importance of a data scientist is only meant to rise. So, if you think you are up for the challenges that dealing with loads of data poses, welcome to the world of data science. All the best!
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