When you ask someone what five things they can’t live without, they usually respond with food, a car, and the internet. When you ask a data scientist the same question, they’ll give you a rundown of their top five analytics tools, the ones that make work and life easier to manage. Let’s have a look at what these analytics tools are and what they do in a little more detail:
1. Python
Python is a popular general-purpose programming language that is simple to learn, has less lines of code than other languages, is very legible, and is open source. It has a well-developed and expanding ecosystem of open source mathematics and data analysis tools, making it a good contender for the title of “tool of the future.” It’s lightning fast and comes with a large collection of statistical data. It is one of the languages with which a large number of programmers are familiar, allowing for a smooth move into analytics from an IT standpoint.
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It is a skill to learn if one wants to move into the analytics sector from a programming background. It has only recently gained popularity among professionals in the analytics domain, so there are fewer job openings, but it is definitely a skill to learn if one wants to move into the analytics sector from a programming background. Python makes coding and debugging easier because to its better syntax, which results in a much shorter learning curve.
Pros
- Python’s straightforward syntax makes it simple to learn. Many programmers are already familiar with Python, and they find it easier to learn Python for analytics than a new language like R.
- Python is a completely free programming language.
- Python’s statistical libraries have been fast expanding, making it a rather versatile tool presently.
Cons
- Python has just lately made the shift from a programming language to an analytics tool. As a result, it lacks the versatility of R and SAS.
- Python is quickly gaining traction in the analytics field. Python’s popularity will only grow as more IT programmers migrate towards analytics. Python is unquestionably a tool worth learning.
2. Excel for a Data Scientist
Microsoft Excel is a spreadsheet programme that is included in the Microsoft Office suite of productivity software. We’ve all used it to make lists and tables at some point in our lives, whether in school or in college. Excel, however, is capable of much more. Excel has a wide range of capabilities, including sorting and manipulating data as well as presenting it in graphs and charts. It can execute a wide range of arithmetic operations, including those related to statistics, engineering, and finance. It also allows you to programme using VBA (Visual Basic for Application).
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Due to its ubiquitous availability, Excel is one of the easiest data tools to learn and use. There aren’t many computers that don’t have MS Office (both premium and free) and, by extension, MS Excel installed. The most significant advantage of Excel is that it allows users to alter GUIs (graphical user interfaces) and do a reasonable level of data visualization (nothing too complex though). While it can manage tiny amounts of data, it is not designed to handle big amounts of data or do tasks such as predictive modelling.
Nonetheless, it is still one of the most extensively used data manipulation tools available, and it will benefit every aspiring data scientist. It also features a very user-friendly interface for non-technical users who want to dabble in data research.
Pros
- Excel is a programme that everyone is familiar with. Even if they don’t have any additional analytics software, most users have Excel installed on their computers.
- Excel is a user-friendly programme. The user interface is simple and easy to use.
- Excel has a lot of visualisation possibilities.
Cons
- Excel isn’t designed for complex statistical analysis. Simple predictive modelling techniques such as clustering and regression can be conducted in Excel with the help of add-ons, but more complicated approaches such as machine learning are not.
- Excel can manage over 16000 columns and 1 million rows. Dealing with even 100,000 rows and 1000 columns, on the other hand, is excruciating.
- If you execute a pivot on that much data, for example, Excel gets slow and may crash.
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3. SAS
SAS is a software suite for advanced analytics, predictive modelling, business intelligence, and data management developed by SAS Institute. Despite its reputation for being difficult to use and understand, SAS, unlike many of its competitors, can handle a wide range of data management and analytics jobs. It’s great for power users, and it’s one of the world’s most reliable and quick analytics software suites, as well as one of the best for complex analysis.
Despite the fact that its pricing and licencing are a sore point, many mid- to large-sized businesses still use it for the sheer processing power it provides. Despite its lack of visualisation, it is still the go-to tool for complicated data analysis on massive datasets.
Pros
- SAS is a powerful tool that can handle small to large data sets can be used for everything from simple slice and dice analysis to complex multi-variate analysis.
- SAS comes with a lot of online help.
Cons
- It’s a pricey piece of equipment. SAS licences (including the non-GUI versions) might be as expensive as or more than hiring a data scientist.
- Visualization is limited.
To get started with SAS, go to SAS Data Science for Beginners and learn how to become a certified data scientist.
4. R
R, a computer language and software environment for statistical computing and graphics, is SAS’s most formidable rival. Because of its open source status, it has strong fans. It is an outstanding tool that can perform any type of statistical analysis. Nothing makes geeks happier than open source and free-to-try software. R allows users to tailor the software to their own analytics needs, and it comes with a robust package ecosystem that makes working with it even easier.
It has been increasingly robust since its inception, and it now has a vibrant community of users that help one another. For any organisation that does not have analytics at its core but nonetheless works with data, R is the way to go. It’s the best software for doing repeatable and high-quality analyses. It is still a very good analytics tool, despite its security and memory management shortcomings.
Pros
- R is a flexible language. Some users believe it is now even more flexible than SAS. R users rarely need to use any other software.
- R is free because it is open source.
- R works nicely with the open source technologies that are prevalent in the big data world.
Cons
- The learning curve for R is quite severe. It is a difficult tool to master.
- While there is a lot of information on the Internet, it isn’t as well organised as, example, SAS materials.
Start with our Data Science with R certification course to add R to your analytics toolkit.
5. SQL
SQL (Structured Query Language) is a special purpose programming language that is used to interface with and administer databases, specifically in an RDBMS (relational database management system) or RDSMS (relational database system management system). It’s simple to understand and apply, yet it’s been utilised to address a variety of difficult situations.
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While it isn’t the best tool for statistical analysis, it is one of the best for data manipulation and can handle big data sets. Data manipulation still takes up roughly half of the project’s time, and SQL fits right in. It easily interacts with and reads unstructured data, and it works well with both old and new databases.
Pros
- SQL is lightning quick and can handle data sets of any size.
- Because SQL is used in so many places outside of analytics, most users are already familiar with it.
- SQL is a simple language to grasp.
Cons
- SQL is great for slicing and dicing, but not so much for statistical analysis. As a result, the range of applications is very limited.
Few tools can match SQL’s speed and ease of use when it comes to data manipulation. For data scientists, SQL is a very popular add-on tool. It works nicely with SAS, R, Python, and other programming languages.
So there you have it! These are the five tools that any data scientist should have. How many are you familiar with? How many haven’t made it onto your list yet?
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