Data Analytics vs Data Science
TLDRData science and data analytics are related but distinct fields. Data science is the overarching field encompassing the full process of extracting insights from data, including data collection, cleaning, analysis, modeling, and visualization. Data analytics is a specialization within data science focused more narrowly on querying, interpreting, and visualizing datasets to derive actionable insights. While data scientists create new algorithms and models, data analysts apply existing tools and techniques. Both roles require analytical and technical skills to help organizations understand their data and make better decisions.
Takeaways
- 😀 Data science is the overarching umbrella term that covers tasks like data mining, machine learning model training, and AI application deployment
- 📊 Data analytics is a specialization within data science that focuses on querying, interpreting and visualizing datasets
- 🔬 Data science follows an iterative methodology called the data science lifecycle across 7 phases: problem identification, data extraction, data cleaning, analysis, feature engineering, predictive modeling, and visualization
- 📈 Data analysts utilize 4 types of analytics: predictive to identify trends and patterns, prescriptive to recommend decisions, diagnostic to determine why events occurred, and descriptive to evaluate dataset details
- 👩💻 Data scientists need skills in machine learning, AI, Python, R, big data platforms, databases and SQL
- 👨💼 Data analysts require analytical, programming and data visualization competencies along with database and statistical analysis familiarity
- 🛒 Predictive analytics helps forecast events like product inventory sell-outs or regional disease outbreaks
- 🔎 Diagnostic analytics determines the reasons behind occurrences like manufacturing equipment failures
- 📊 Descriptive analytics evaluates dataset metrics like subscriber gains/losses and content viewership
- 📈 Applying data science and analytics enables data-driven decisions around inventory, marketing campaign optimization and more
Q & A
What is the difference between data science and data analytics?
-Data science is the overarching field that covers tasks like finding patterns in data, machine learning, and AI applications. Data analytics is a specialization within data science focused on querying, interpreting, and visualizing datasets.
What are the main phases in the data science lifecycle?
-The main phases are: identify a problem/opportunity, data mining, data cleaning, data analysis, feature engineering, predictive modeling, and data visualization.
What coding languages are useful to know for a career in data science?
-Helpful coding languages for data science include Python, R, SQL, and languages used with big data platforms like Hadoop and Apache Spark.
What are some techniques used in data analytics?
-Key techniques include predictive analytics to identify trends, prescriptive analytics to recommend decisions, diagnostic analytics to determine causes, and descriptive analytics to evaluate qualities of a dataset.
What skills are important for a career as a data analyst?
-Important skills include analytical skills, programming and database knowledge, statistical analysis abilities, and data visualization expertise.
How can business analysts perform data analytics?
-Business analysts can use business intelligence dashboards to conduct business analytics and visualize key performance indicators.
How does the scope of data science compare to data analytics?
-Data science has a broader scope involving creating complex machine learning algorithms. Data analytics focuses more on answering specific questions with existing data.
Why might a company's marketing campaign fail to meet expectations?
-A marketing campaign might fail to meet expectations due to issues that data analysts could uncover, like not targeting the right demographics or ineffective messaging.
How can predictive analytics be used in healthcare?
-In healthcare, predictive analytics can forecast regions expected to experience a rise in flu cases, helping providers prepare.
How can prescriptive analytics be used in manufacturing?
-Manufacturers can use prescriptive analytics to predict when a component might fail and recommend it be replaced ahead of time.
Outlines
😄 Defining and Differentiating Data Science vs Data Analytics
Paragraph 1 defines data science as the overarching field encompassing tasks like data mining, cleaning, analysis, modeling, and visualization to uncover patterns and build AI systems. It characterizes data analytics as a specialization within data science focused on querying, interpreting, and visualizing datasets. The paragraph also outlines the iterative data science lifecycle and the technical skills required for data scientists and analysts.
😃 Roles and Responsibilities of Data Analysts
Paragraph 2 elaborates on the role of data analysts being to interpret and conceptualize existing datasets to drive business decisions. It describes techniques like predictive, prescriptive, diagnostic, and descriptive analytics that analysts employ. The paragraph also contrasts data science's focus on modeling and algorithms vs data analytics emphasis on answering targeted questions.
Mindmap
Keywords
💡data science
💡data analytics
💡data scientist
💡data analyst
💡predictive modeling
💡data visualization
💡data mining
💡machine learning
💡big data
💡data wrangling
Highlights
Data science is the overarching umbrella term that covers tasks related to finding patterns in large datasets
Data analytics focuses on querying, interpreting and visualizing datasets
Data science follows the data science lifecycle with 7 key phases
The 7 phases of the data science lifecycle are: identify problem, data mining, data cleaning, data analysis, feature engineering, predictive modeling, and data visualization
Data scientists need skills in machine learning, AI, Python, R, big data platforms like Hadoop and Spark, databases, and SQL
Data analysts conceptualize datasets using predictive, prescriptive, diagnostic, and descriptive analytics
Data analysts identify trends, correlations, forecast outcomes, make recommendations, pinpoint reasons for events, and evaluate dataset qualities
Data analysts use BI dashboards, conduct analytics, and visualize KPIs
Data analysts need analytical, programming, database, statistical analysis, and data visualization skills
Data science has a broader scope involving complex machine learning models created from scratch
Data analysis focuses on answering specific questions with existing data
Data science focuses on the full pipeline from data collection to predictive modeling
Data analysts offer actionable insights from interpreting existing data
Data scientists create new algorithms and models
Using data science and analytics helps keep inventory stocked by predicting demand
Transcripts
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