What is data science?

Oh, look, another blog about data science. But we have a good reason for it. Data is an ever-expanding discipline and one of the most in-demand careers in the coming years. Canada faces a shortage of 19,000 people with data and analytical skills. Until that gap is closed (and beyond), we won't stop encouraging those interested in data to enter the field.

What is data science in simple terms?

Data science is an interdisciplinary field that combines techniques from statistics, computer science, domain expertise, and data visualization to gather valuable insights from complex and large data sets. These insights are then analyzed to predict future trends and understand current ones, which help businesses, policymakers, or governments make the best decisions.

It involves collecting, cleaning, analyzing, and interpreting data to inform decision-making and solve complex problems. Data science encompasses various methods, tools, and technologies to transform raw data into actionable insights that drive strategies, innovation, and research.

The History of Data Science Discipline

The earliest anyone conceptualized an aspect of data science came in the form of theoretical artificial intelligence. In the late 1600s, Gottfried Wilhelm Leibniz, a German mathematician and philosopher, theorized that human thought could be quantified and thus programmed into a machine. At the time, he was ridiculed as logic was considered a strictly spiritual mechanism.

Leibniz may have been a few centuries ahead of his time, but the real history of data science started around 1962 when John Turkey guessed that the eventual evolution of data analytics was science. In the 1970s, Peter Naur first coined the term "data science." By the 1990s, companies were actively collecting consumer information through various data pipelines and toying with the idea of databases.

The rest is as follows:

Early Stages (Pre-2000s)

Data analysis primarily revolved around traditional statistical methods, with statisticians and analysts working on structured datasets using tools like spreadsheets and statistical software.

Big Data and Digital Revolution (2000s)

The proliferation of the internet and digital devices led to an explosion of data, giving rise to the term "big data." This period saw the need for new tools and approaches to handle and analyze vast and diverse datasets. Hal Varian, Google's Chief Economist, predicts that statisticians will be in high demand, and the ability to process data will be a fundamental skill.

Foundations of Data Science (2010s)

The term "data science" gained popularity as organizations started recognizing the value of data-driven decision-making. The focus shifted toward utilizing advanced algorithms, machine learning, and data visualization techniques.

Modern Era (2020s)

Data science is now deeply integrated into various industries, influencing everything from healthcare and finance to marketing and technology. It continues to evolve with advancements in artificial intelligence (AI), deep learning, and automation.

Differences Between Data Science and Data Analytics

While data science and data analytics share similarities, they have distinct focuses and objectives within data analysis.

Data Science

• Objective: Data science seeks to uncover patterns, insights, and trends in data to drive decision-making and solve complex problems.
• Process: It encompasses the entire data lifecycle, including data collection, cleaning, analysis, modelling, interpretation, and communication of results.
• Techniques: Data science employs advanced statistical modelling, machine learning algorithms, predictive modelling, and data mining to make future predictions and generate actionable insights.
• Application: Data science is often used for innovation, research, and developing predictive models that can guide strategic decisions.
• Skill Set: Data scientists require programming, machine learning, domain knowledge, and data visualization expertise.

Data Analytics

• Objective: Data analytics primarily focuses on examining past data to identify trends, patterns, and historical insights.
• Process: It involves analyzing structured data using various statistical techniques and tools to draw conclusions about the past.
• Techniques: Data analytics involves descriptive statistics, basic querying, and reporting to summarize historical data and answer specific questions.
• Application: Data analytics is used for understanding historical performance, optimizing operations, and making data-driven decisions based on historical data.
• Skill Set: Data analysts need proficiency in statistical analysis, data querying, data visualization, and reporting tools.

In short, data science uses tools to predict future trends and derive insights from large data sets. Data analytics looks into the past to uncover patterns and optimize operations.

What does a data scientist do?

A data scientist is a professional who employs various techniques to analyze and interpret complex data sets to extract valuable insights and inform organizational decision-making. This role requires a unique blend of skills from different domains, including statistics, computer science, domain expertise, and data visualization. Here's an overview of what a data scientist does:

1. Data Collection and Cleaning

• Gather and assemble raw data from various sources, including databases, APIs, and external datasets.
• Clean and preprocess the data to remove inconsistencies, errors, and missing values that could affect analysis.

2. Exploratory Data Analysis (EDA)

• Conduct initial exploratory analysis to understand the data's structure, patterns, and distributions.
• Visualize data using charts, graphs, and summary statistics to identify potential trends and outliers.

3. Feature Engineering

• Select, transform, and create relevant features from the raw data to enhance the performance of machine learning models.

4. Model Building and Training

• Develop and implement machine learning algorithms and statistical models tailored to the specific problem at hand.
• Train models on historical data to learn patterns and make predictions or classifications.

5. Model Evaluation and Optimization

• Evaluate model performance using metrics such as accuracy, precision, recall, and F1-score.
• Fine-tune models by adjusting parameters and exploring different algorithms to improve their effectiveness.

6. Data Visualization and Communication

• Present insights and findings using visualizations that are easy to understand for technical and non-technical stakeholders.
• Communicate complex results clearly and concisely through reports, presentations, and dashboards.

7. Predictive Analysis and Decision-Making

• Leverage models to make predictions about future outcomes, enabling proactive decision-making.
• Provide actionable recommendations to stakeholders based on data-driven insights.

8. Machine Learning Deployment

• Collaborate with software engineers to deploy models into production environments, ensuring seamless integration with existing systems.

9. Continuous Learning and Improvement

• Stay up-to-date with the latest advancements in data science, machine learning, and related technologies.
• Continuously refine and update models as new data becomes available.

Why is Data Science Important for an Organization?

Data science plays a pivotal role in the modern business landscape, enabling organizations to harness the power of data and gain a competitive edge. Here's why data science is essential:

1. Informed Decision-Making

• Data-driven insights help organizations make well-informed decisions based on empirical evidence rather than intuition or guesswork.

2. Predictive Analytics

• Data science enables organizations to predict future trends, customer behaviour, and market shifts, aiding in proactive planning.

3. Efficiency and Optimization

• By analyzing data, organizations can identify inefficiencies, streamline operations, and optimize processes to save time and resources.

4. Personalized Experiences

• Data-driven insights allow organizations to tailor products, services, and marketing strategies to individual customer preferences.

5. Risk Management

• Data science helps assess and mitigate risks by identifying potential pitfalls and developing contingency plans.

6. Innovation and Research

• Data scientists explore data to discover new opportunities, research areas, and innovative solutions that drive growth.

7. Competitive Advantage

• Organizations that effectively leverage data science can gain a competitive advantage by staying ahead of market trends and customer demands.

8. Customer Insights

• Data analysis provides deep insights into customer behaviour and sentiment, aiding in understanding their needs and enhancing satisfaction.

9. Resource Allocation

• Data-driven insights guide the efficient allocation of resources by identifying areas that require more investment and those that can be streamlined.

10. Adaptation to Change

• Organizations can adapt to changing market conditions and customer preferences by relying on data-driven insights for quick adjustments.

Tools and Resources in Data Science

Check out the video below for a deeper look into data science and which tools complete specific actions.

Data science relies on a variety of tools and resources to handle, analyze, and visualize data effectively. Some commonly used tools include:

1. Programming Languages

• Python: Widely used for data manipulation, analysis, and building machine learning models using libraries like pandas, NumPy, and scikit-learn.
• R: Especially popular for statistical analysis and data visualization, with packages like ggplot2 and dplyr.

2. Data Visualization Tools

• Matplotlib: A Python library for creating static, interactive, and animated visualizations.
• Seaborn: Built on top of Matplotlib, Seaborn provides enhanced visualizations with fewer lines of code.
• Tableau: A powerful data visualization tool for creating interactive and shareable dashboards.

3. Data Manipulation and Analysis

• pandas: A Python library for data manipulation, cleaning, and analysis using data structures like DataFrames.
• SQL: Structured Query Language for managing and querying relational databases.
• Excel: Often used for basic data analysis and visualization.

4. Machine Learning and AI Libraries

• scikit-learn: A versatile Python library for machine learning algorithms and model selection.
• TensorFlow: An open-source framework for building and training deep learning models.
• PyTorch: Another deep learning framework is known for its dynamic computational graph.

5. Big Data and Distributed Computing

• Hadoop: An open-source framework for processing and storing large datasets across clusters.
• Spark: A fast and versatile data processing engine for big data analytics.

6. Version Control

• Git: A distributed version control system for tracking changes in code and collaborating with teams.

Different Aspects of Data Science

Data science comprises various interconnected aspects that collectively contribute to its success:

1. Data Collection and Preparation

• Gathering relevant data from diverse sources and unstructured data, including databases, APIs, and external datasets.
• Cleaning and preprocessing data to ensure its quality and consistency.

2. Exploratory Data Analysis

• Understanding the data structure through summary statistics, visualization, and identifying patterns.

3. Feature Engineering

• Selecting and transforming data features to enhance model performance and predictive accuracy.

4. Model Building and Selection

• Developing and implementing machine learning algorithms and statistical models suitable for the problem.
• Evaluating different models to identify the best-performing one.

5. Model Training and Validation

• Training models on historical data while avoiding overfitting and assessing their performance using validation techniques.

6. Predictive Analytics

• Using models to make predictions about future outcomes enables proactive decision-making.

7. Data Visualization and Communication

• Creating visual representations of data to effectively convey insights and findings to stakeholders.

8. Deployment

• Collaborating with software engineers to integrate models into production systems for real-time decision-making.

9. Interdisciplinary Domain Knowledge

• Incorporating expertise from various fields, such as business, healthcare, finance, etc., to understand the data's context.

10. Ethical Considerations

• Addressing ethical concerns related to privacy, bias, and fairness in data collection, analysis, and model deployment.

11. Continuous Learning and Adaptation

• Staying updated with the latest tools, techniques, and trends in data science to remain effective and innovative.

Is data scientist an IT job?

While data science often involves working with technology and data-related tasks, it is not strictly considered an Information Technology (IT) job in the traditional sense. Data science is a multifaceted field that combines skills from various domains, including statistics, mathematics, computer science, domain expertise, and communication. While IT jobs primarily focus on managing and maintaining information technology systems, data science specializes in extracting insights and making predictions from data.

Curious about a career in data science? Check out our Data Science Program and satisfy your curiosity in just a few clicks.