Data Science is the new powerhouse driving the industry to greater heights by processing Big Data with Machine Learning methods. Today, organizations are utilizing Data Science processes to detect hidden patterns, increase efficiencies, manage costs, and identify new market opportunities. Development in Machine Learning and Neural Network algorithms have evolved many applications and eventually resulted in a significant rise in varieties of Data roles like Data Engineer, Data Science, and more.
However, this article provides a comprehensive overview of the processes involved in Data Science. It also explains the importance of different tools and recent advancements that have lowered the barrier by computing complex problems. Lastly, it provides insights into challenges faced and an opinion for Data Science as a career.
Table of Contents
Introduction to Data Science
Data Science is a multidisciplinary field combining Statistics, Programming, and domain expertise to derive applications for solving business problems. The term Data Science has emerged with the evolution of Big Data, Computation Power, and Statistics. The core job of Data Scientists is to manifest solutions to create a positive impact by providing practical solutions. As organizations attempt to become Data-driven, Data Science endeavours the key to success in the competitive market.
Understanding the Lifecycle of Data Science
The core job of Data Scientists is to address problems and construct Models to make better decisions for multifaceted business challenges. Data Science processes involve the following steps:
1) Defining Business Problem
The most critical step in this process is creating a well-defined problem statement to understand business challenges. Any ambiguity in the initial phase of problem definition will result in loss of time and money, mostly due to Project failures. Defining business problems involves formulating a hypothesis that helps understand clear and goal-oriented tasks by asking the right questions.
2) Data Collection and Preparation
Data Collection is a systematic process of gathering relevant information from various sources. It is always recommended to ensure the quality of Data before performing Analysis, as it may give misleading information. However, Data Collection is carried out considering domain expertise and past experiences.
Seldom is Data generated in a structured and noiseless form, thereby causing dilemmas in Data Analysis and Model Developments. Data Preparation consumes a maximum amount of time as it involves Statistical Techniques, Anomaly Detection, and Data Transformation. It eventually illuminates whether features are independent of each other to avoid collinearity and balancing Data biases.
3) Exploratory Data Analysis
Exploratory Data Analysis (EDA) helps build familiarity with Data and extract valuable insights. Data Scientists traverse through unknown Data to uncover patterns and derive beholding relations among Data points. To perform EDA, Data Scientists leverage statistics and visualization tools to summarize central measures and variability.
If Data persists with skewness, suitable transformations are applied to scale distribution around its mean. Exploring Data can be strenuous when Datasets consist of numerous features. Consequently, to reduce the complexity of Model inputs, feature selection is carried out to rank them, considering their importance in Model Building.
4) Model Building
In Model Building, Data Scientists use Data prepared through EDA to build various Machine Learning Algorithms. Model selection depends on the type of business problem and can be approached using Supervised or Unsupervised learning techniques.
Data collected is split into three parts — Train, Test, and Validation. The Model is built using Train Data to recognize patterns. A Model should be trained with a colossal amount of Data to avoid biases and enable better results.
5) Model Optimization
To improve the accuracy of prediction after Model Development, certain iterations are carried out to optimize the performance. A Model is selected based on the least variance with its train and validation results. However, some Models might need hyperparameter tuning to get the best performance for given input features.
A Model, despite tuning, can be inefficient in several cases, requiring domain expertise to enhance the performance. In such cases, Data is investigated further by performing feature engineering to optimize the results.
6) Model Deployment and Evaluation
Since Models learn from Training Data, it may result in overfitting. To address the overfitting problem, various evaluations are performed by feeding unseen Data called Test Data. A Model is evaluated with numerous Metrics and Validation methods like nested Cross-Validation, K-fold Cross-Validation, and LOOCV, and results are often concluded by observing the Confusion Matrix.
The Best-Performing Model is brought in the production phase as a Pilot Project by deploying it in real-time. If the Model passes the testing phase, it is scaled to perform huge operations with full deployment.
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6 Key Tools used in Data Science
Data Science requires a combination of Programming and Analytical tools to build Models that can help develop superior AI-based solutions. To carry out Data Science tasks, professionals need frequent use of the following tools:
1) Statistics and Probability
Data Science is built on the foundations of Statistics and Probability. Probability Theory is quite useful in formulating predictions. Data Science relies heavily on Estimations and Projections. We make Estimations for further examination with the use of Statistical approaches. As a result, Statistical approaches are heavily reliant on Probability Theory. All the Statistics and Probability is based on Data.
2) Data Visualization Skills
People skim and skip the most essential stories in the newspaper, but the ones that people read are mostly Sketches. It is a human concept to see something and have it registered in one’s mind. The complete Dataset, which may number in the hundreds of pages, can be reduced to two or three Graphs or Plots. To create Graphs, one must first visualize the Data Patterns. Microsoft Excel is a fantastic program that generates the appropriate Charts and Graphs based on our requirements. Tableau, Metabase, and Power BI are some examples of other Data Visualisation and Business Intelligence tools.
SQL (Structured Query Language) is a standard Programming Language used to fetch Data to perform Exploratory Data Analysis, making it useful in insights generation and Model Development. In any organization, Data Collection and Analysis are performed by querying vast Databases with SQL.
4) Big Data
Processing Big Data is complex and may require plenty of time with traditional tools. As collected Data are primarily Unstructured, tools such as Apache Hive are frameworks integrating SQL on top of Hadoop to allow working with Unstructured Information. It allows users to read, write, and efficiently process petabytes of Data.
5) Programming Language
Data Analysis and Model Development are done using Programming Languages like Python, R, and various other tools. It helps in carrying out Statistical Tests and advanced transformations to obtain quality Data. Eventually, the Data is used to build Models to automate strenuous business operations.
One of the most widely used Programming Languages in Data Science is Python, it provides several packages and libraries for building Machine Learning or Deep Learning Models with ease. However, the R Programming Language is better for Statistical Analysis and Data Exploration. Different Programming Languages are subtle to perform different tasks and should be embraced accordingly to the needs.
Data Scientists are likely to present their findings to non-technical audiences or Business Executives. The most important thing is to understand the viewers and speak in their language. To ensure there are no communication gaps, Data Scientists should focus on values and outcomes while aligning them with the interest of stakeholders.
Advancements in Data Science
In recent times, Machine Learning and Artificial Intelligence have scaled many organization’s initiatives with precise outcomes. The following are a few recent advancements in Data Science:
- AutoML: Automation of Machine Learning tasks is one of the most challenging initiatives in Data Science. However, companies like DataRobot and H2O.ai have established themselves by providing state-of-the-art AutoML solutions to help organizations perform end-to-end Machine Learning processes.
- Data Privacy: Privacy and Security of Data are always sensitive, any hindrance may result in losing trust in future business deals. The entire Data Science process is fueled by Data, hence it becomes a priority to eliminate Data leakage by implementing privacy-preserving techniques like Federated Machine Learning. It is often applied to decentralize Data servers and train heterogeneous Datasets, thereby avoiding Data Leaks, Data Pooling, and reducing the need for other Cloud resources.
- MLOps: Among many aspects of Machine Learning, MLOPs has positively impacted organizations by adding value to AI-based Project Management. It assists in the Scalability, Tracking, and Auditing of Models during Development. MLOps not only mitigate risks of inaccurate predictions by optimizing Models but also automate Machine Learning Pipelines to accelerate the Project life cycle.
- NLP: Advancements in Deep Learning have enhanced the capabilities of integrating Natural Language Processing (NLP) into Data Science. Huge Datasets of text or audio can be transformed into either numerical Data or broken down into tokens for Standard Analysis. The Development of Transformer Architecture created a new baseline for NLP-based Deep Learning approaches. Transformer became the foundation of BERT’s (Bidirectional Encoder Representations from Transformers), a pre-trained general Model, which can incorporate new techniques in NLP through fine-tuning.
For further information on Advance Machine Learning techniques, visit here.
Challenges in Data Science Technologies
- Most Data Science Projects fail to positively impact business processes due to poor Data quality.
- Data Scientists lack the skills to explain the technical jargon and complexities of a Model, leading to the loss of stakeholder’s interest.
- Organizations lack security checks to safeguard their Data and Models, making them vulnerable to Cyber Attacks and Adversarial Attacks, respectively.
- Many companies have a misconception of this role and expect Data Scientists to be the jack of all Data processes.
Unravelling Data Science as a Career
Since Data Science includes numerous skills like Programming, Statistics, Domain knowledge, and Storytelling, it can be frightening, especially at the beginning. However, the demand for Data Science is rising rapidly because of the wider use case in day-to-day applications. Current trends show several opportunities for both freshers or experienced Data enthusiasts. As a result, employers are investing a significant amount of time and money while hiring the right Data Scientists.
Along with the tools used to perform Model Analysis and Development, this article talks about the entire lifecycle of the Data Science Project. It also emphasizes the impact of advancements in Data Science and the current challenges in this field.
The first step in implementing any Data Science Algorithm is integrating the Data from all sources. However, most businesses today have an extremely high volume of Data with a dynamic structure that is stored across numerous applications. Creating a Data Pipeline from scratch for such Data is a complex process since businesses will have to utilize a high amount of resources to develop it and then ensure that it can keep up with the increased Data Volume and Schema Variations. Businesses can instead use automated platforms like Hevo.
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