Learn about Data Science & its interesting branches and acknowledges the essential tools to become an expert in it
Before diving into Data Science, let us know something about data. Data is a collection of values or figures which are usually numerical, collected through direct observations or can be extracted from websites. In early days, people used to file data in the format of hard copy (engraving on stones, writing on leaves and papers etc.) hence, the scope of data analysis was severely low. People analyzed these data papers manually with their pair of eyes and of course, this case of analyzing is only possible for small data. After finding some difficulty in this, they brought a set of mathematical and statistical calculations for valuation and analysis of their data and this is where Data Science was born.
Coming to the question, what is Data Science, it can be defined as a scientific or a mathematical method of analyzing data in a more technical sense. As the evolution of Computers shifted to programming and software tools, Data Science started taking a new phase. At present, Data Science is considered among the top-5 in-demand skills which bags you a job and also it is one of the fastest growing chains. Although, the perspective of data analysis shifts from programming to automation in recent days, the base ideology of Data Science remains the same. To become an expert as a Data Scientist, it is imperative to master the fields of Data Mining and Data Visualization. You're welcome to read below to know about Data Mining and Data Visualization along with their best software tools which help in boosting your career.
Data Mining
In simple words, Data Mining is defined as the process used to extract usable data and discover patterns of datasets. Data Mining implies the methods of Machine Learning, Statistics and so on to perform analyzing Data Patterns.
What is Data Pattern and why is it important? Data Pattern tells us about an overview of our dataset like the way it has been extracted, about its structure etc. Every dataset is unique and the recognition of these patterns in the cardinal data is important. For example, if a business wishes to produce crystal clear results of a dataset, it is essential to identify the Data Patterns in the underlying dataset to choose the algorithm and techniques which are most appropriate to perform particular type of data mining and analysis. The job market for Data Mining is in high demand giving most priority to these softwares:
R Programming Language
Python
RapidMiner
R Programming Language
R Programming language is an open-source software developed by statisticians and it is widely used among Data Miners for developing Data Analysis. R can be best programmed and developed in RStudio which is an IDE (Integrated Development Environment) for R. The advantages of R programming language include quality plotting and eye catching reports, vast source of packages used for various functions, exemplary source for Data Wrangling, performance of various Machine Learning operations and flashy for Statistics. In my experience, R is the best language for self-learning and also it is very accessible for Financial Analysis and Graphing. You can use R also for creating Trading Bots which helps you with profiting in Intraday Trading. However, it also has some cons including lesser speed and it has a weak origin meaning it does not provide dynamic functions with its base packages and it is necessary to install external packages to perform 3D graphings and dynamic functions. Notwithstanding the disadvantages, R is still very handy and powerful for Statistical Computing and Data Mining.
Python
Python is a highly-advanced programming language used for general purposes and also it is very useful for Data Mining. Python is very popular among coders because it is highly productive and has the capability to automate specific tasks in an efficient way. Also, it is highly recommended for people who are stepping into programming as the syntax format for coding as it is comparatively simple. So, as a beginner, we can accomplish many things in Python. When it comes to functions for Data Mining, Python is widely used by Data Miners as it is highly productive, also, it provides the programmer with a vast source of machine learning functions which helps in creating algorithms for specific tasks. What lags in Python? When comparing with R, which approaches a statistical way of computing towards Data Mining whereas, Python approaches it with a general form.
Should I learn R or Python ?
In general, you shouldn't be choosing between R and Python, instead, you should learn both for better insight. Investing your time into acquiring practical knowledge of the two languages is worth the effort for multiple reasons. Having both in your resume strengthens your perception towards Data Mining and boosts your Data Science career.
RapidMiner
RapidMiner is a Data Science software which provides an integrated environment for data preparation, machine learning, data mining and predictive analytics. This software provides the users with a unified approach which gives businesses to think out of the box in boosting their efficiency and productivity. The key features of RapidMiner include, the tools in RapidMiner which support powerful potentiality, at the same time, it provides user-friendly interface to the users which helps in performing productively in their works. The biggest problem associated with RapidMiner is that it doesn't work well with Big Data as it requires a lot of memory but still, it's the most preferred and powerful software for Data Mining.
Data Visualization
Data Visualization is the graphic representation of Data. It involves producing efficient visual elements like charts, dashboards, graphs, mappings etc. so as to give an accessible way of understanding trends, outliers and patterns of data to people. The state of achieving people's mind depends on our creativity in visualizing data and by maintaining a communicative relationship between audience and the represented data. In recent days, the job market for Data Visualization is comparatively high giving most priority to these softwares :
Tableau
Power BI
Sisense
Tableau
Tableau is interactive and it is one of the fastest growing Data Visualization and Business Intelligence tools, which aims at people to see and understand data. In simple words, Tableau turns a raw data into an understandable story like the ones above. The biggest plus for Tableau is that it doesn't require any sort of coding for creating a model and can be learnt very easily. Also, it provides the users with a structured approach for crystal clear Data Visuals. So, beginners who learn Data Visualization are highly recommended to begin with Tableau. However, the problem with Tableau is about its high pricing features and one can't import external models into it. Nonetheless, the rush of companies towards Tableau for Data Visualizations is still high.
Power BI
Power BI is a Data Visualization and Business Analytics platform which aims to provide interactive visuals and Business Intelligence with an interface for users to create custom reports and dashboards. Power BI is a subsidiary of Microsoft and the most preferred software by companies for data representations. The eye-catching advantage of Power BI is that it's an open-source software and provides customers in creating their contrived visual framework. Like Tableau, Power BI is easy to learn and most preferred for beginners. The disadvantages are one, Power BI is not comfortable with very large data. Second, it's comparatively complex in nature which means it has a long list of components and it is difficult to understand. In Data Visualization job market, Power BI appears to be a trump card for companies as it is cost-efficient and provides immersive visuals.
Sisense
Sisense is a Business Intelligence (BI) and a Data Visualization platform which provides the user with advanced tools to manage and represent data with analytics and visuals. Sisense supports companies to analyse big data efficiently and generates pertinent business models. The advantages of Sisense include that it uses the memory storage of computers with a systematic approach and it has a script-less user interface which gives customers a feel of ease of use. The drawbacks of Sisense are that it needs to simplify the way of sharing dashboards or charts and it should improvise its mobile platform. Putting your time into learning Sisense is worthwhile as it is the leading Business Intelligence (BI) software.
Career Scope in Data Science
Data Science is everywhere and is going to be the prominent field in every sector. Also, it is one of the fastest growing chains so, obviously, it is going to be a benchmark in future. So, the world needs adequate data scientists in future but there's going to be heavy competition for this role. To clear the competition and to stand out of the crowd, one needs to be an expert in it and not just mastering merely a few aspects. Neglecting things won't last in a long run.
To put things in perspective, not only data mining and visualization is the important aspect of Data Science but also there are many more to dive inside for better insights. The main thing is not to limit your path towards Data Science and aspire to reach as far as you can. My perspective towards Data Science is that don't treat Data Science as another subject but have passion to do wonders with it. Many would come across and suggest you to learn only a thing for better insight but that won't last forever. For example, if you mastered Python what happens if the world shifts its perspective of Data Science from programming languages to automation all of a sudden? So, start your data science career with an ideal goal, create your own agenda of what you are going to learn, invent things, think outside of the box and be creative.
HAPPY ANALYZING !