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You need to have several technical and non-technical skills to become a successful Data Scientist. Some of the skills are essential to have to become a well-versed data scientist while some for just for making thing things easier for a data scientist. Different job roles determine the level of skill-specific proficiency you need to possess.
Given below are some skills you will require to become a data scientist.
Technical Skills
Python
Data Scientists use Python a lot because it is one of the most popular programming languages, easy to learn and has extensive libraries that can be used for data manipulation and data analysis. Since it is a flexible language, it can be used in all stages of Data Science, such as data mining or running applications. Python has a huge open-source library with powerful Data Science libraries like Numpy, Pandas, Matplotlib, PyTorch, Keras, Scikit Learn, Seaborn, etc. These libraries help with different Data Science tasks, such as reading large datasets, plotting and visualizing data and correlations, training and fitting machine learning models to your data, evaluating the performance of the model, etc.
SQL
SQL is an additional essential prerequisite before getting started with Data Science. SQL is relatively simple compared to other programming languages, but is required to become a Data Scientist. This programming language is used to manage and query relational database-stored data. We can retrieve, insert, update, and remove data with SQL. To extract insights from data, it is crucial to be able to create complicated SQL queries that include joins, group by, having, etc. The join method enables you to query many tables simultaneously. SQL also enables the execution of analytical operations and the transformation of database structures.
R
R is an advanced language that is used to make complex models of statistics. R also lets you work with arrays, matrices, and vectors. R is well-known for its graphical libraries, which let users draw beautiful graphs and make them easy to understand.
With R Shiny, programmers can make web applications using R, which is used to embed visualizations in web pages and gives users a lot of ways to interact with them. Also, data extraction is a key part of the science of data. R lets you connect your R code to database management systems.
R also gives you a number of options for more advanced data analysis, such as building prediction models, machine learning algorithms, etc. R also has a number of packages for processing images.
Statistics
In data science, advanced machine learning algorithms that stores and translate data patterns for prediction rely heavily on statistics. Data scientists utilize statistics to collect, assess, analyze, and derive conclusions from data, as well as to apply relevant quantitative mathematical models and variables. Data scientists work as programmers, researchers, and executives in business, among other roles, all of these disciplines have a statistical foundation. The importance of statistics in data science is comparable to that of programming languages.
Hadoop
Data scientists perform operations on enormous amount of data but sometimes the memory of the system is not able to carry out processing on these huge amount of data. So how data processing will be performed on such huge amount of data? Here Hadoop comes in the picture. It is used to rapidly divide and transfer data to numerous servers for data processing and other actions such as filtering. While Hadoop is based on the concept of Distributed Computing, several firms require that Data Scientists have a fundamental understanding of Distributed System principles such as Pig, Hive, MapReduce, etc. Several firms have begun to use Hadoop-as-a-Service (HaaS), another name for Hadoop in the cloud, so that Data Scientists do not need to understand Hadoop”s inner workings.
Spark
Spark is a framework for big data computation like Hadoop and has gained some popularity in Data Science world. Hadoop reads data from the disk and writes data to the disk while on the other hand Spark Calculates the computation results in the system memory, making it comparatively easy and faster than Hadoop. The function of Apache Spark is to facilitate the speed of the complex algorithms and it is specially designed for the data science. If the dataset is huge then it distributes data processing which saves a lot of time. The main reason of using apache spark is because of its speed and the platform it provides to run data science tasks and processes. It is possible to run Spark on a single machine or a cluster of machines which makes it convenient to work with.
Machine Learning
Machine Learning is crucial component of Data Science. Machine Learning algorithms are an effective method for analysing massive volumes of data. It may assist in automating a variety of Data Science-related operations. Nevertheless, an in-depth understanding of Machine Learning principles is not required to begin a career in this industry. The majority of Data Scientists lack skills in Machine Learning. Just a tiny fraction of Data Scientists has extensive knowledge and expertise in advanced topics such as Recommendation Engines, Adversarial Learning, Reinforcement Learning, Natural Language Processing, Outlier Detection, Time Series Analysis, Computer Vision, Survival Analysis, etc. These competencies will consequently help you stand out in a Data Science profession.
Non-Technical Skills
Understanding of Business Domain
More understanding one has for a particular business area or domain, easier it will be for a data scientist to do the analysis on the data from that particular domain.
Understanding of Data
Data Science is all about data so it is very important to have an understanding of data that what is data, how data is stored, knowledge of tables, rows and columns.
Critical and Logical Thinking
Critical thinking is the ability to think clearly and logically while figuring out and understanding how ideas fit together. In data science, you need to be able to think critically to get useful insights and improve business operations. Critical thinking is probably one of the most important skills in data science. It makes it easier for them to dig deeper into information and find the most important things.
Product Understanding
Designing models isn”t the entire job of a data scientist. Data scientists have to come up with insights that can be used to improve the quality of products. With a systematic approach, professionals can accelerate quickly if they understand the whole product. They can help models get started (bootstrap) and improve feature engineering. This skill also helps them improve their storytelling by revealing thoughts and insights about products that they may not have thought of before.
Adaptability
One of the most sought-after soft skills for data scientists in the modern talent acquisition process is the ability to adapt. Because new technologies are being made and used more quickly, professionals have to quickly learn how to use them. As a data scientist, you have to keep up with changing business trends and be able to adapt.
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