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Guide to a Career in Data Science
Published
05th Sep, 2023
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Data Science is a buzzword in today’s world. Data engineers, data scientists, and data programmers often talk about data science. To put it in simple words, Data Science is an interdisciplinary field where we explore, research, and extract some knowledge out of the structured and unstructured data.
The process of exploration, research, and extraction involves a significant scientific method or principle, relative algorithms, and various statistical mathematics to perform on vast amounts of data to get meaningful insights from it. This data that is extracted and further used by companies or organizations to draw insights for their business goals or solutions.
Every organization today uses data science directly or indirectly, be it giant conglomerates across industries ranging from aerospace to banking and even government bodies.
Applications of Data Science
Data Science Components
- Statistics: Statistics is a field of Mathematics, which helps in quantifying a large amount of numerical data and helps in analyzing meaningful outcomes.
- Visualization: Visualization is the graphical representation of data in a graphical format like Line chart, Pie chart, and many more so that it’s easy to understand the trends and patterns which are also used for the purpose of building predictive models.
- Algorithms: There are many algorithms which support various business problems like predictions, classifications, segmentations, recommendations, object detection and image classifications.
- Data engineering: Data engineering is a separate field, but the work of Data Engineers helps Data Scientists get structure and filtered data. Extraction, Load and Transformation (ETL) or Extractions, Transformation & Load (ETL) forms a key activity under data engineering.
Prerequisites to a career in Data Science
There are certain prerequisites required for an individual to start a career in Data Science which will be discussed below.
Prerequisites to a career in Data Science
As denoted in the above graph, Data Science is the combination of multiple fields, however, few of them are very prominent and are required as prerequisites like Mathematics, Computer Science basic, and a certain knowledge on Domain expertise.
As Data Scientists deal with the analytics of both structured and unstructured records, in both numeric and alphabetic format, some need to have a basic understanding of statistics because most of the analytical work requires a statistical approach to solve the data science problem.
For implementing the solution while applying a statistical approach, one needs to have a basic understanding of programming languages like Python and R which are very prominent for Data Science.
Domain expertise will help gather a deep understanding of certain businesses like banking and finance to solve related use cases. The first step in Data Science is data discovery on a specific data set, which in turn gives access to data on the specific domain or business. This data extracted is then used by the data scientist to project useful insights about the industry that helps business leaders take or make appropriate strategies to benefit the overall business.
Apart from this there are other fields like Machine Learning, which need an in-depth knowledge of core computer science topics like data structure and algorithms that are designed specially to mine the data, cluster the data, and perform other operations of Machine Learning, Deep Learning, and Artificial Intelligence.
Artificial intelligence is one of the fields where one needs to have a good grasp of statistical mathematics and core computer science concepts. As a beginner, it can be quite challenging to gain expertise in each of these fields because Data Science is a very vast field.
Data Science Life cycle
Data Science Life cycle
Business Understanding
Having a business understanding is also one of the vital characteristics of data science. Data scientists need to understand the purpose of their role and also to ask the right questions.
For example, in the banking domain, if the leadership team wants to do the prediction and forecasting of their banking product, the data scientist needs to have a clear understanding of the banking business model and their relevant products, how this product works, and what kind of data or information is associated with this. They need to understand the accurate customer details to look for, how this data is classified, and how one can use the same to make a prediction. Similarly, many other examples can be applied to different domains or industries where business knowledge is required to predict and identify the right customers.
Data Collection
Data discovery is one of the crucial steps in Data Science and one needs to understand the source of data. This data source usually varies for different domains.
Let’s take the example of the banking business, here, the data is generally saved in a data warehouse or RDBMS or in a private cloud, and to gather this, one requires approval as it is highly classified data. Another example would be of the online retail business, the data for this is usually available on the web or online media using which one can understand consumer behavior and what kind of products they are interested in. In a nutshell, data scientists need to know how to gather data from different sources.
Data Preparation
Data extraction, also called ETL, is how one extracts, transforms and loads the data. The correct data from the source needs to be extracted and standard transforms are performed. This includes data cleaning, which is the removal of unwanted records that do not have any relevance to data analytics; and data standardization, which is preparing the data according to the required format by various machine learning algorithms.
Data Modeling
In data modeling, data scientists use a statistical approach to get trends, apply data mining, classification, clustering, and other advanced tools like machine learning, deep learning, and AI-based algorithms.
One of the many things you might need to do in modeling is to reduce the dimensionality of your records set. Not all your features or values are important for predicting your model. What you want to do is to select the relevant ones that contribute to the prediction of results. There are a few duties we can perform in modeling. We can also teach models to perform classification of emails you obtained as “Inbox” and “Spam” using logistic regressions. We can also forecast values using linear regressions. We can use modeling of organization information to apprehend the logic behind those clusters. For example, as for an e commerce institution to recognize the behavior of its users on its website, it needs to identify organizations of record points with clustering algorithms like k-way or hierarchical clustering.
Let’s take the example clustering algorithms, which are generally used to explore the trends and create an individual group from the huge volume of a dataset, these individual groups are formed based on clustering algorithms so that each group has individual trends which are analyzed by the Data scientist. An Machine Learning expert can go beyond that and perform more complex algorithms on the same and get a prediction beyond that. Generally, they use the predictive analysis algorithm and supervised learning algorithm which is performed on a high volume of historical data and perform the iterative train on the model, which is further used to build the prediction.
Interpreting Data
Now we got the resultant dataset, so now the next step is how to interpret the resulting data, so that management can understand and take the executive decision accordingly. Generally, the interpretation happens by exploring it and constructing graphs. When you are dealing with massive volumes of statistics, visualization is the first-class way to explore and communicate your findings and is the next segment of your records analytics project. Now the big catch here is, how to communicate to the leadership or management team and effectively convey the result is one in all the most underrated abilities a data scientist can have. While several data scientists ought to have the ability to communicate with other teams and effectively translate their work for maximum impact. This set of skills is frequently called ‘information storytelling.’ You take the statistics on the present-day possibilities that the income crew is pursuing, run it through your model, and rank them in a spreadsheet within the order of most to least likely to convert. You provide the spreadsheet for your VP of Sales.
Practical
In this session, we will talk about some of the prominent algorithms, which are implemented in most of the Data science projects.
Linear regression
Linear regression is one of the highly adaptable algorithms when it comes to the prediction, Linear regression is used in supervised learning, which comes under the Machine learning use case.
This algorithm works on iterative approach where we are targeting the model values based on an independent dataset and calculate the closer, which thus forms the linear equation. In layman terms, this helps to form the relationship between input values and target output. As stated earlier, this algorithm helps to do the predictive analysis. Below is the equation for the same.
Y= MX+C
Where, y= Dependent variable
X= independent variable
M= slope
C= intercept.
K-Means Clustering
K-means clustering, an unsupervised learning algorithm, is another prominent algorithm of machine learning, which generally performs clustering using the historical dataset. This algorithm is useful in instances when we have a data set of items to be categorized into groups. This method requires a good understanding of statistical mathematics.
Application of Data Science
Thanks to faster computing and cheaper storage, we can now predict outcomes in minutes that would take several human hours to process. In this section, we’ve rounded up seven examples of data science at work, across industries from gaming to healthcare.
Image and Speech Recognition
Image reorganization is generally applied in social media when the algorithm helps the user match and find friends for any given suggestion. Speech recognition is mostly seen on mobile handsets like SIRI for the iPhone, where you get to give instructions to SIRI to perform a task.
Gaming
Machine learning algorithms are used widely in gaming to capture and analyze the user experience and enrich features and gaming functionalities.
Internet Search
Internet search engines like Google, Bing, and Yahoo capture user behavior and refine the search data as per the keyword so that the most frequently visited page ranks on top.
Transport or Maps navigation system
Google maps show many routes from point A (source) to point B (target). When a user finds a new way, Google map trains the model again, so that it can now add on a new route. The map navigation too detects the pattern of driving and calculates the time frame to reach the destination.
Healthcare
Healthcare has seen some of the most prominent implementations of Data Science. Drug discovery, tumor detection, breast cancer detection, medical image analysis, and many more key applications have demonstrated the importance of data science in this field.
Recommendation Systems
Recommendation systems are one of the more profitable systems, mostly used by online retail companies to analyze the user’s purchasing behavior. The data gathered helps the system come up with suggestions of relevant products that the customer may be interested to purchase.
Banking and Finance
The Banking and financial institutions predominately apply the Data science approach to calculate the credit score, while providing the loans to customers. This helps banks and financial institutions to minimize the risk of non-payment. A similar approach is adopted by credit card companies as well.
Conclusion
The Data Science field is one of the booming technologies, and as per Gartner prediction the scope of this field will be there till the next 10-15 years and many discoveries will be taking birth in the field. Data Science can be used to increase productivity in many fields and inventions in the manufacturing field and self-driving cars only stand to prove this right.
However, the negative consequence of this is that it will proportionally decrease human intervention which can cause great unemployment. Finding a balance between how much automation or artificial intelligence is required, can leverage both human and artificial intelligence to go hand-in-hand. With the way data science is growing presently, it is evident that there will always be a scope for a data scientist as every business is looking for growth.
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