Introduction to Data Science

 

What is Data Science?

The term "data science" inherently suggests the application of scientific methods or mathematical formulas to manipulate data. With data being generated incessantly, often in petabytes, there's an increasing need for various methods and algorithms to extract insights and facilitate decision-making. Data science integrates principles from statistics, computer science, and domain-specific knowledge to analyze datasets and operational efficiency in businesses and organizations. Crucially, data science thrives on its interdisciplinary nature. Data scientists possess a diverse skill set encompassing good amount of knowledge in programming languages like Python or R, and a profound grasp of data visualization techniques as data visualization plays an important role in it.

Where is data stored?

Data from different places like transaction systems, databases, and even social media or IoT devices are all stored in a data warehouse.

Data warehouse:- It's like a big organized storage space. This warehouse brings all this information together, making it easy for everyone to see and understand. So, decision-makers can use this data to make smart choices because they have a clear picture of what's going on.

Data mart:- A data mart is a subset of a data warehouse that is focused on a specific functional area or department within an organization, such as sales, marketing, finance, or human resources. It contains a tailored collection of data that is relevant to the needs of users within that particular department or business unit. Data marts are often created to address the unique requirements and priorities of different business units or departments. They allow users to access and analyze data relevant to their specific area of focus without having to navigate through the entire data warehouse.

Data lake:- A data lake is a vast storage repository that holds large volumes of raw, unstructured, or semi-structured data in its native format. Unlike traditional databases or data warehouses, which structure data before storing it, a data lake stores data as-is, without any predefined schema or organization.

The data science process encompasses several important stages:-

• Data Collection:- Collecting data from diverse sources including databases, APIs, sensors, or through web scraping.
• Data Cleaning and Preprocessing:- Preparing the data for analysis by addressing missing values, eliminating outliers, and transforming variables as required.
• Exploratory Data Analysis (EDA):- Conducting thorough analysis and visualization of the data to find underlying patterns and relationships.
• Model Building:- Formulating statistical models or machine learning algorithms aimed at predicting outcomes, classifying data, or uncovering patterns.
• Model Evaluation and Validation:- Assessing the efficiency of models using metrics such as accuracy, precision.
• Deployment and Implementation:- Integrating the validated models into real-world systems or applications to automate decision-making processes or enhance existing workflows.

This systematic approach underscores the meticulousness with which data scientists approach their work, emphasizing the importance of robust data preparation, exploratory analysis, and rigorous model evaluation. In essence, this above information serves as a comprehensive introduction to data science, illustrating its critical role in today's data-centric world and providing a structured framework for understanding its core principles and methodologies.

What is ETL process?

The ETL (Extract, Transform, Load) process stands as a cornerstone in the realm of data warehousing and analytics. It facilitates the movement of data from its origins to designated destinations like data warehouses or data lakes. Here's a detailed breakdown of each phase:

Extract (E):-

• In the extraction phase, data is fetched from various source systems such as databases, applications, files, or APIs.
• Data extraction methods may encompass full extraction, gathering all data from the source, or incremental extraction, fetching only new or modified data since the last extraction.
• Extracted data often arrives in diverse formats and structures, necessitating transformation for uniformity and compatibility.

Transform (T):-

• Following extraction, data undergoes transformation to ensure uniformity, quality, and suitability for analysis.
• Data transformation contains numerous operations like cleaning, filtering, deduplication, standardization, and normalization.
• Additionally, data may be enhanced by merging it with other sources, summarizing it, or deriving new attributes.
• Transformation rules and logic are applied, often utilizing scripting languages, SQL queries, or specialized ETL tools.

Load (L):-

• The final phase of the ETL process involves loading transformed data into the designated destination, such as a data warehouse, data mart, or data lake.
• Data loading strategies may vary depending on the destination and requirements, including bulk loading, incremental loading, or real-time streaming.
• Loading data into the target system typically involves mapping transformed data to the appropriate tables or structures, ensuring data integrity and consistency.
• Data loading may also encompass indexing, partitioning, or other optimization techniques to enhance query performance and usability.

The ETL process is a cyclical and ongoing endeavor. As data sources evolve and new requirements surface, continuous monitoring and maintenance are important to ensure the accuracy, completeness, and timeliness of the data loaded into the target system.

Furthermore, with the rise of big data technologies and cloud computing, the ETL process has evolved to consider additional factors such as scalability, elasticity, and real-time processing capabilities. This evolution has led to the emergence of modern data integration platforms and frameworks adept at handling vast volumes of data across distributed environments with efficiency.

Basically, the ETL process is like the glue that holds everything together in managing and analyzing data. It helps organizations take data from different places, change it into a format that's easy to understand, and put it all in one place. This makes it easier to study the data, create reports, and make important decisions.

ETL helps in improving data quality, consistency, and accessibility, ETL enables organizations to make informed decisions, drive operational efficiency, and gain a competitive edge in today's data-driven landscape.

Challenges with ETL

• While ETL is essential, with this exponential increase in data sources and types, building and maintaining reliable data pipelines has become one of the more challenging parts of data engineering. 
• From the start, building pipelines that ensure data reliability is slow and difficult. 
• Data pipelines are built with complex code and limited reusability. 
• A pipeline built in one environment cannot be used in another, even if the underlying code is very similar, meaning data engineers are often the bottleneck and tasked with reinventing the wheel every time. 
• Beyond pipeline development, managing data quality in increasingly complex pipeline architectures is difficult. 
• Bad data is often allowed to flow through a pipeline undetected, devaluing the entire data set. 
• To maintain quality and ensure reliable insights, data engineers are required to write extensive custom code to implement quality checks and validation at every step of the pipeline. 
• Finally, as pipelines grow in scale and complexity, companies face increased operational load managing them which makes data reliability incredibly difficult to maintain. 
• Data processing infrastructure has to be set up, scaled, restarted, patched, and updated - which translates to increased time and cost. 
• Pipeline failures are difficult to identify and even more difficult to solve - due to lack of visibility and tooling. 
• Regardless of all of these challenges, reliable ETL is an absolutely critical process for any business that hopes to be insights-driven. 
• Without ETL tools that maintain a standard of data reliability, teams across the business are required to blindly make decisions without reliable metrics or reports.
•  To continue to scale, data engineers need tools to streamline and democratize ETL, making the ETL lifecycle easier, and enabling data teams to build and leverage their own data pipelines in order to get to insights faster.

Best practices of ETL process:-

• Never try to cleanse all the data:- Every organization would like to have all the data clean, but most of them are not ready to pay to wait or not ready to wait. To clean it all would simply take too long, so it is better not to try to cleanse all the data.
• Determine the cost of cleansing the data:- Before cleansing all the dirty data, it is important for you to determine the cleansing cost for every dirty data element
• To speed up query processing, have auxiliary views and indexes:- To reduce storage costs, store summarized data into disk tapes. Also, the trade-off between the volume of data to be stored and its detailed usage is required. Trade-off at the level of granularity of data to decrease the storage costs.