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Data Science Foundations & Learning Path
Published
26th Apr, 2024
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In the age of big data processing, how to store these terabytes of data surfed over the internet was the key concern of companies until 2010. Now that the issue of storage of big data has been solved successfully by Hadoop and various other frameworks, the concern has shifted to processing these data. From website visits to online shopping, transitions from cell phones to browsing computers, every little thing we search online forms an enormous source of business industry data.
The pandemic has led to an increase in data science demand as the world has shifted in pursuit of the "new normal" from offline to online. But what is Data Science? What are its salient characteristics? Where are we going to learn more about this? Let's take a look at all the fuss about data science, its courses, and the path to the future.
What is Data Science?
In order to discover insights and then analyze multiple structured and unstructured data, Data Science requires the use of different instruments, algorithms and principles. This is achieved using different methods and languages that we will eventually address in the alternative portion.
Predictive causal analytics, prescriptive analytics and machine learning are some tools used to make decisions and predictions in data science.
- Predictive causal analytics: When lending your friends money, do you ever wonder if they're going to give it back to you or not? Or are you making predictions that are the same? If so, then this is exactly what casual predictive analysis does. In the future, it estimates the possibilities of a real occurrence that may or may not happen. This tool helps businesses measure the probability of such events, such as whether or not purchases made by a customer will be on time.
- Prescriptive analytics: Back in the 2000s, people admired flying vehicles. Today, when self-driven vehicles are already on the market, we have entered a point where we do not even need to drive a vehicle. How was this possible? If you want a model that has the intelligence to make its own choices and the ability to change it with dynamic parameters, what is needed is prescriptive analytics. This helps to make a decision based on the predictions of a computer programme. The best thing is that, the best course of action to take is advised for a certain situation.
- Machine learning for making predictions: Machine Learning (ML) is a computer programme framework that allows algorithms and is capable without human intervention of taking decisions and generating outputs. Known to be one of the most powerful and important technological advances in recent times, machine learning has already enabled us to conduct real-world calculations and analytics, something that would have taken years to solve through traditional computing. For example, it is possible to plan and train a fraud detection model, using the past records of fraudulent transactions.
- Machine learning for discovery of pattern: If you don't have the parameters you can forecast, you need to figure out the secret trends in the dataset in order to be able to make any predictions that are meaningful. Clustering, a technique in which data points are grouped together according to the similarity of their characteristics and patterns, is the most used algorithm for pattern discovery.
Suppose you work in a telephone company, for instance, and you are expected to set up a network by building towers in an area. In this case, to locate the tower positions, you can use the clustering technique to ensure that all users obtain the maximum signal power.
The Base For Data Science
Though data scientists come from different backgrounds, have different skills and work experience, most of them should either be strong in it or have a good grip on the four main areas:
- Business and Management
- Statistics and Probability.
- B.Tech(Computer Science) Or Data Architecture.
- Verbal and Written Communications.
Based on these foundations, we can conclude that a data scientist is a person who has the expertise to extract some useful knowledge and actionable insights from data,by managing complicated data sources and the above areas. The knowledge we receive can be used to make strategic business decisions and to make improvements necessary to achieve business objectives.
This is done by the use of experience in the business domain, efficient communication and analysis of findings and the use of some or all of the related statistical techniques and methods, databases, programming languages, software packages, data infrastructure, etc.
Data Science Goals and Deliverables
Let's look at the paradigms that data science has proven to succeed in. There are different fields in which data science has been extremely beneficial. Data scientists set certain targets and results to be accomplished by the data science process. Let's discuss them in brief:
- Prediction
- Classification
- Recommendations
- Pattern detection and classification
- Anomaly detection
- Recognition
- Actionable insights
- Automated processes and decision-making
- Scoring and ranking
- Segmentation
- Optimization
- Forecast Sales
All of these are intended to address specific problems and solve it.
Many managers are highly intelligent people, but they may still not be well versed in all the instruments or techniques and algorithms available (e.g., statistical analysis, machine learning, artificial intelligence, etc.). Therefore, they might not be able to tell a data scientist what they want as a final deliverable, or recommend the sources, features and the right direction to get there from the data sources.
Therefore an ideal data scientist must have a reasonably detailed understanding of how organisations function in general and how data from an organisation can be used to achieve top-level business objectives. With exceptional experience in the business domain, a data scientist should be able to constantly discover and recommend new data projects to help the organisation accomplish its objectives and optimise its KPIs.
Data Scientists vs. Data Analysts vs. Data Engineers
Like several other related positions, the role of data scientist is most frequently misunderstood. Data Analysts and Data Engineers are the two key ones, both very distinct from each other as well as from Data Science.
Let us look into how they are different from one another so that we may have a clear understanding of all these different job roles and profiles.
Data Analyst
Data analysts have many skills and responsibilities similar to a data scientist, and sometimes even have a similar educational background as well. Some of these similar skills include the ability to:
- Access and query (e.g., SQL) different data sources
- Process and clean data
- Summarize data
- Understand and use statistics and mathematical techniques
- Prepare data visualizations and reports
Some of the distinctions, however are that computer programmers are not data analysts, nor are they accountable for mathematical modelling or machine learning, and several other measures explained above in the data science process.
The various instruments used are often typically different. Data analysts typically use analytical and business intelligence software such as MS Excel, Tableau, PowerBI, QlikView, SAS, and may also use a few SAP modules. Analysts also do data mining and modelling tasks occasionally, but typically prefer to use visual tools for data science activities, such as IBM SPSS Modeler, Rapid Miner, SAS, and KNIME.
Data scientists, on the other hand, usually perform the same tasks with software such as R or Python, together with some relevant libraries for the language used. Data scientists are also more accountable for teaching linear, non-linear algorithms in mathematical models.
Data Engineer
Data scientists also use data from different sources, which are then collected, transformed, combined, and ultimately processed in a manner that is optimized for analytics, business intelligence, and modelling.
On the other hand, computer engineers are responsible for the design of data and the setting up of the necessary infrastructure. They need to be competent programmers with some skills that are very similar to those necessary in a DevOps job, and with good and powerful writing skills for data query.
Another main aspect of this position is database design (RDBMS, NoSQL, and NewSQL), data warehousing, and setting up a data lake. This means they need to be very familiar with many database technology and management systems available, including those associated with big data (For example, Hadoop, Redshift, Snowflake and Cassandra).
The Data Scientist’s Toolbox
Data scientists should be proficient with such programming languages such as Python, R, SQL, Java, Julia, Apache Spark and Scala, as computer programming is a huge part. Usually, in all of these, it's not important to be an expert programmer, but Python or R, and SQL are certainly the main languages they should be familiar with.
Some useful and famous data science courses which you can definitely avail to strengthen your knowledge and concepts are as follows :
- Data Science Specialization from KnowledgeHut
- Introduction to Data Science from Metis
- Applied Data Science along with Python Specialization from the University of Michigan
- Dataquest
- Statistics and Data Science Micro Masters from MIT
- CS109 Data Science from Harvard
- Python for Data Science and Machine Learning Bootcamp
Some of the many courses available online related to Data Science are the courses listed above. At the end of the course, all the courses provide you with a certificate of completion. These courses will, above all other advantages, help you develop a database on data science and eventually move you to a level where you will be fully prepared to deal with some real data!
Conclusion
Data science has become an important part of today's generation. Even the tiniest move we take on the internet leaves our digital footprint and extracts information from it. Having expertise in the processing of data science can help you go a long way. Perhaps it's not unfair to suggest that Data Science would control a large portion of our future.
Data scientists have a huge positive effect and impact on the performance of a company, but sometimes they may also cause financial losses, which is one of the many reasons why it is important to employ a top-notch data scientist. However it can bring prosperity, effectiveness and sustainability to any organisation if implemented in a perfect manner.
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