Data Science is the Future
A Data Scientist is responsible for extracting, manipulating, pre-processing, and generating predictions out of data. In order to do so, he requires various statistical tools and programming languages. New sources of data are emerging every day. The Internet of Things is talked about a lot today because of the large flow of data from Sensors in manufacturing processes to vehicles. A lot of this data will be time series based and have its own set of unique problems. Even though automated systems will play a key role, the human element will still be necessary for data science in the future. Mobile Devices have come to accommodate Artificial Intelligence (AI) in their operating systems. Personal assistants like Siri, Cortana, and Google now make use of machine learning and AI logic which all depend on data sciences. Whichever language you choose to learn, it has its own advantages in Data science applications.
Best Data Science Tools
Data Scientists use standard statistical methodologies that form the core backbone of Machine Learning algorithms. They additionally use Deep Learning algorithms to generate strong predictions. Data Scientists use the following tools and programming languages:
- R
R is a scripting language that is particularly tailored for statistical computing. It is extensively used for statistical analysis, statistical modeling, time-series forecasting, clustering, etc. R is basically used for statistical operations. It also possesses the aspects of an object-oriented programming language. R is an interpreter-based language and is extensively popular throughout multiple industries.
- Python
Like R, Python is an interpreter primarily based on the high-level programming language. Python is a versatile language. It is mainly used for Data Science and Software Development. Python has received popularity due to its ease of use and code readability. As a result, Python is extensively used for Data Analysis, Natural Language Processing, and Computer Vision. Python comes with various graphical and statistical programs like Matplotlib, Numpy, SciPy, and more advanced applications for Deep Learning such as TensorFlow, PyTorch, Keras, etc. For the cause of data mining, wrangling, visualizations, and developing predictive models, we make use of Python. This makes Python a very flexible programming language.
- SQL
SQL stands for Structured Query Language. Data Scientists use SQL for managing and querying information stored in databases. Being in a position to extract data from databases is the first step toward analyzing the data. Relational Databases are a collection of data organized in tables.
We use SQL for extracting, managing, and manipulating the data. For instance, A Data Scientist working in a banking enterprise uses SQL for extracting information from customers. While Relational Databases use SQL, ‘NoSQL’ is a popular preference for non-relational or distributed databases. Recently NoSQL has been gaining recognition due to its flexible scalability, dynamic design, and open-source nature. MongoDB, Redis, and Cassandra are some of the popular NoSQL languages.
- Hadoop
Big data is another trending term that deals with the management and storage of huge amounts of data. Data is either structured or unstructured. A Data Scientist must have a familiarity with complex data and must-know tools that regulate the storage of massive datasets.
One such tool is Hadoop. While being open-source software, Hadoop utilizes a distributed storage system using a model called ‘MapReduce’. There are several packages in Hadoop such as Apache Pig, Hive, HBase, etc. Due to its ability to process colossal data quickly, its scalable architecture, and low-cost deployment, Hadoop has grown to become the most popular software for Big Data.
- SAS
It is one of these data science tools which are mainly designed for statistical operations. SAS is closed source proprietary software that is used by large companies to analyze data. SAS makes use of the base SAS programming language for performing statistical modeling. It is widely used by professionals and corporations working on reliable business software.
SAS affords severe statistical libraries and tools that you as a Data Scientist can use for modeling and organizing your data. While SAS is pretty reliable and has robust help from the company, it is relatively expensive and is only used by larger industries. Also, SAS pales in comparison with some of the more modern tools which are open-source. Furthermore, there are various libraries and packages in SAS that are no longer available in the base pack and can require an expensive upgrade.
- Tableau
Tableau is Data Visualization software specializing in the graphical analysis of data. It permits its users to create interactive visualizations and dashboards. This makes Tableau the best choice for displaying various developments and insights of the data in the form of intractable charts such as Treemaps, Histograms, Box plots, etc. An essential feature of Tableau is its ability to connect with spreadsheets, relational databases, and cloud platforms. This permits Tableau to process information directly, making it simpler for the users.
- Apache Spark
Apache Spark or simply Spark is an all-powerful analytics engine and it is the most used Data Science tool. Spark is specifically designed to handle batch processing and Stream Processing. It comes with many APIs that facilitate Data Scientists to make repeated access to data for Machine Learning, Storage in SQL, etc. It is an improvement over Hadoop and can perform 100 times faster than MapReduce. Spark has many Machine Learning APIs that can help Data Scientists to make powerful predictions with the given data.
Spark does better than other Big Data Platforms in its ability to handle streaming data. This means that Spark can process real-time data as compared to other analytical tools that process only historical data in batches. It offers various APIs that are programmable in Python, Java, and R. But the most powerful conjunction of Spark is with Scala programming language which is based on Java Virtual Machine and is cross-platform in nature. It is also highly efficient in cluster management which makes it much better than Hadoop as the latter is only used for storage. It is this cluster management system that allows Spark to process applications at a high speed.
- Matplotlib
Matplotlib is a plotting and visualization library developed for Python. It is the most popular device for generating graphs with the analyzed data. It is primarily used for plotting complex graphs and the use of simple lines of code. Using this, one can generate bar plots, histograms, scatterplots, etc. It has numerous fundamental modules. One of the most extensively used modules is pyplot. It presents a MATLAB-like interface. Pyplot is also an open-source alternative to MATLAB’s graphic modules.
Matplotlib is a preferred tool for data visualizations and is used by Data Scientists over other current tools. As a matter of fact, NASA used Matplotlib for illustrating data visualizations during the landing of Phoenix Spacecraft. It is additionally a perfect tool for beginners in gaining knowledge of data visualization with Python.
- Weka
For Data Scientists looking ahead to getting familiar with Machine Learning in action, Weka can be a perfect option. Weka is commonly used for Data Mining but also consists of various tools required for Machine Learning operations. It is absolutely open-source software that makes use of GUI Interface making it simpler for users to have interaction with, besides requiring any line of code.
- TensorFlow
TensorFlow has become a well-known tool for Machine Learning. It is extensively used for advanced machine learning algorithms like Deep Learning. Developers named TensorFlow after Tensors which are multidimensional arrays. It is an open-source and ever-evolving toolkit that is recognized for its overall performance and excessive computational abilities.
TensorFlow can run on both CPUs and GPUs and has recently emerged on more powerful TPU platforms. Due to its high processing ability, Tensorflow has a range of applications such as speech recognition, image classification, drug discovery, image and language generation, etc. For Data Scientists specializing in Machine Learning, Tensorflow is a must known tool.
Summary
In the globalized world of today the opportunities are rising subsequently day by day. There are openings for the profile of data scientist worldwide and it is no hidden fact that the salary packages provided to the candidates are skyrocketing. This is a field where the career opportunities are lots so having Data Science training and expertise on your aspect in this domain can go a long way for the candidate.
New sources of data are rising each and every day. The Internet of Things is talked about a lot today due to the fact of large flow of information from Sensors in manufacturing processes to vehicles. A lot of this data will be time series based and have its own set of unique problems. Even though automated systems will play a key role, the human factor will still be essential for data science in the future.
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