Introduction to Data Science
Data Science is creating tremendous opportunities for certified and skilled data analysts and data scientists as it is growing rapidly with emerging technologies around the world. It launches many innovative things and equipment that simplify the efforts of humans and rectify human errors effectively. A career in Data Science becomes the best thing for freshers and those who want to transform their careers into a data analytics platform. But there are numerous top-notch skills to be improved and they are the key points to prove as best analyst or data scientist in big companies. We have collected them from industrialists and given here the must-have Data Science skills required for a candidate to enter into the data science domain.
Technical Skills
Python as highest consideration
The in-depth knowledge of Python programming language along with mathematical and statistical skills is considered the most important to become a data scientist. Most of the survey reveals that Python is growing in popularity as it is user-friendly for developers, testers, scriptwriters, data scientists, and analysts. The learning of the Python programming language brings a promising start in the data science platform as it simplifies complicated work like algorithm creation and implementation. Other programming languages like R, SAS, and SQL are added advantage to perform as per the client’s expectations.
Strong in Python Library usage
Python programming language has tons of libraries to implement where it is repeatedly called functions and mundane coding. The library usage quickens the development and implementation of the project and most of the Python Libraries are easy to use, interpret, and implement. Python libraries can provide useful insights to perform the data correlation and data integration effectively. There are some popular frameworks and libraries in Python that work efficiently for machine learning processing, deep learning concepts, and data analytics. Some of them are TensorFlow, PyTorch, Theano, and Keras and they are used mostly in the process of solving the complexity of advanced problems in data science.
Understanding of GPU hardware and CUDA
Data Science processing depends on the hardware as it is used for implementing to solve most complicated deep learning models, machine learning algorithms, and artificial intelligence works that include neural network, NLP (Natural Language Processing), Image processing, and pattern works. The hardware knowledge is important to install and implement powerful machines that contain highly configured CPUs to perform these scientific computations. GPU (Graphical Processing Unit) along with CUDA (Compute Unified Device Architecture) is used to perform and accelerate scientific computations and data analytics effectively with speed and accuracy. The deep knowledge in GPU and CUDA along with hands-on practice helps the candidate to get a job easily in big firms for working as data scientists.
In-depth understanding of algorithms
Numerous algorithms such as logical regression, linear regression, decision trees, SVM algorithms, Naïve Bayes, KNN algorithm, K-means algorithm, and random forest algorithm, dimensionality reduction algorithms, and gradient boosting algorithm and AdaBoosting algorithm are popularly utilized in data science process and implemented in various projects. 71% of data scientists are implementing these algorithms for performing various scientific computations and numerical calculations.
An acquaintance in Cloud Service Providers
Big companies are adapting to cloud practice from on-premise solutions for easy access, cost-effectiveness, and data security. A situation like Pandemic makes employees work from home and it relies on Cloud Computing for providing uninterrupted solutions to users. The knowledge in Cloud Service providers is the need of the hour and nearly 70% of companies using AWS, 20% of companies using Azure and many companies are using other small providers as per their investments.
Proficiency in Visualization tools
Data Visualization is the main part of the data science process as it displays the data in an understandable format to clients as well as industry leaders. There are some frequently used tools in the market such as Tableau, PowerBI, and MSBI, and so on. The learning of visualization tools helps the candidate to perform a data visualization process that becomes useful for decision-making purposes.
Knowledge of Github usage
Data Scientists are utilizing this Github Platform to collaborate and contribute their projects for the benefit of beginners and enterprise project managers. There is a wide range of projects available in Github and many of them are available as open-source. Finding a suitable project that can be useful for developing projects is helpful for the data scientist to fasten the development and deployment.
Well-versed with available IDEs
IDE (Integrated Development Environment) is used to write, debug, and implement codes, and sometimes data scientists require to develop a code or some for their projects. These IDEs are used to complete the debugging process, resource management, and another related process. Notebook, PyCharm, and RStudio are widely used by data scientists around the world and the knowledge of them is useful for beginners to perform the same.
Hadoop Expertise
Big Data Analytics is the recent trend as the world generates trillions of data every day through applications and website usage. These large amounts of data to be structured for the decision-making process and it offer a cost-effective solution by using the tools like Hadoop. Therewith, the expertise in Hadoop is considered a must-have skill, and learning them will be helpful for the students to sustain strongly in data science platform.
End Note
Demand for data scientists and data analysts are growing every day and you can check them in job portals. All the job descriptions contain the above skills and gaining expertise in these areas helps attain the job easily in big companies. The certification along with hands-on exposure is required in all areas and top institutes in Chennai provide them to bridge the knowledge gap of global industries.
