4 Technical Skills You Need To Have for Data Science Using Python Training

Data Science has become one of the hottest and most revered professions in today’s technology market all across the globe. A course in Data Science Using Python training is an excellent decision for both beginners as well as experienced data analysts who aim to pursue Data Science as a career or are aiming to get a comprehensive, in-depth understanding of using Python for analyzing data.

What makes Python popular and prevalent in the Data Science community is the fact that it is one of the most preferred and loved programming languages used in almost everything, everywhere. The inherent readability and simplicity of this language, the dedicated analytical libraries available under Python, and the extensibility and general-purpose nature of this programming software make it a jack of all trades, and a much-applauded choice with data scientists. ‘

python for data science

However, for a Data Science using Python course, it is a must that you have knowledge of Python beforehand. This course is especially recommended for analytics professionals who wish to work with Python, IT professionals who intend to make a career in analytics, software professionals who are thinking of switching over to the analytics field, graduates who are looking to make a career in Analytics and Data Science, experienced professionals wanting to utilize Data Science in their present fields, or anyone having a keen interest in the Data Science segment.

4 Technical skills required for pursuing a course in Data Science using Python training are-

Python Coding- Data Science roles use Python as the most common coding language, along with Java, Perl, and C/C++. Data scientists find Python a great language to work with and hence use it primarily as their major programming language. All the different steps involved in Data Science processes can be easily incorporated using Python due to its versatility. Moreover, with Python, one can easily import SQL tables into their code, and it can even take various formats of data. Also, you can create any kind of dat-set with Python and find it on Google too.

Hadoop Platform- Having a prior inkling regarding ‘Hive’ or ‘Pig’ works wonders, and is heavily preferred, although it’s not a compulsory prerequisite for Data Science using Python training. Knowing Cloud tools, like Amazon S3, is also quite advantageous. The function of Hadoop comes in when the volume of data exceeds the memory of your system or when data needs to be transmitted to different servers. Hadoop allows quick transmission of data to various points in a system. Further, it can also be used for data filtration, data exploration, data sampling, and summarization.

SQL Database Coding- Knowing, writing, and executing complex queries in SQL is expected of a candidate wishing to get training in Python for Data Science. Structured query language, or SQL, as it is commonly known, is a programming language which lets you execute operations such as delete, extract or add data from a database, convert database structures, and carry out analytical functions. Data Scientists, therefore, need to be proficient with SQL as it assists in accessing, communicating, and working on data.

It provides insights for querying on a database. Also, its concise commands let you save on time and reduce the quantum of programming required for performing diversified queries. In a crux, SQL gives you a better understanding of relational databases and even boosts your CV as a data scientist.

Apache Spark- If you talk about big data technology, Apache Spark tops the list worldwide in popularity. Like Hadoop, this too is a big data computation framework. However, Hadoop reads and writes to the disk, which makes it slower, while Spark caches its computations to memory. This enables running of complicated Data Science algorithms faster, disseminates data processing when working with a huge quantum of data, and even saves on time.

With Apache Spark, complex unstructured data-sets can be conveniently handled by Data Scientists as it can be used on one or multiple machines simultaneously. The knowledge of Apache Spark also prevents loss of data by data scientists owing to its speed and easy platform for carrying out Data Science projects. Hence, right from carrying out the analytics from data intake to distributing computing, everything can be incorporated with the knowledge and usage of Apache Spark for Data Sciences.

Conclusion

Learning Data Science using Python training, by enrolling for a coaching program, can teach you how to analyze data, use powerful machine learning algorithms, convert data into meaningful statistics, and even create beautiful visualizations. It will not only help in understanding the core concepts of Data Sciences but will also render first-hand knowledge with practical expertise required in the field of data mining, information management, and visualization.

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