Data Analytics

Data Science

Focus and Scope

The primary focus of data analytics courses is on analysing and interpreting data to find patterns and insights for smart business decisions.

A broader range of subjects are covered in data science courses, including data analysis, machine learning, statistical modelling, and programming to create predictive models and algorithms.

Skills Emphasized

Data cleaning, data visualisation, descriptive statistics, business intelligence, and reporting are among the skills that are emphasised in data analytics courses.

Statistical analysis, machine learning, programming (using languages like Python or R), data mining, data visualisation, and big data technologies are some of the skills that are presented in data science courses.

Depth of Statistics and Mathematics:

The core statistical and mathematical ideas required for descriptive analysis and comprehending data trends are often covered in data analytics courses.

Advanced mathematics (linear algebra, calculus, probability) and inferential statistics for predictive modelling and hypothesis testing are covered in-depth in data science courses.

The difficulty of algorithms

Courses on data analytics may not address the creation of complicated algorithms, instead focusing on the application of already-developed algorithms and methodologies to analyze data and produce insights.

The creation and application of complex algorithms, such as machine learning, deep learning, and optimization methods, is covered in data science courses.

SOFTWARE AND TOOLS

Data analysis, visualisation, and reporting technologies like Excel, Tableau, or Power BI may be the main focus of data analytics courses.

Using tools like Jupyter Notebook, scikit-learn, TensorFlow, or PyTorch for data manipulation, modelling, and analysis is a common assignment in data science courses.

Predictive Modeling:

Although basic predictive modelling strategies may be briefly discussed in data analytics courses, the emphasis is largely on descriptive analysis and comprehending historical data.

Big data technologies (like Hadoop and Spark) and data engineering ideas like data preparation, data pipelines, and distributed computing for managing massive datasets are covered in data science courses.

Experimental Design and A/B Testing:

Although A/B testing and rigorous experimental procedures may not be covered, basic experimental design concepts may be included in data analytics courses.

Principles like experimental design and statistical testing, such as A/B testing, hypothesis testing, and experimental validation, are discussed in data science courses.

Domain expertise

Applying data analysis methods to certain fields, such as marketing, finance, or healthcare, may be the main focus of data analytics courses.

Domain-specific applications may be covered in data science courses, but they also place an emphasis on a wider range of problem-solving abilities that are applicable to many different domains.

Job Roles and Titles:

Graduates of data analytics programmes frequently go on to hold positions with titles like business analyst, data analyst, or business intelligence analyst.

Graduates of data science programmes frequently seek positions with titles like data scientist, machine learning engineer, or data engineer.

Communication and Storytelling:

Though communication skills are discussed in data analytics courses, the emphasis is typically on delivering analytical conclusions to business audiences.

The capacity to successfully communicate complicated discoveries and insights gained from data to multiple stakeholders is frequently stressed in data science courses.

Industry Applications:

Data analytics courses may place more of an emphasis on particular business applications and domains, such as supply chain analytics, financial analysis, and marketing analytics.

Numerous sector applications are covered in data science courses, including those in banking, healthcare, marketing, e-commerce, and social media research.

Time Series Analysis

Basic time series analysis principles may be included in data analytics classes, but advanced approaches are often not covered in as much detail.

Time series analysis methods like forecasting, trend analysis, seasonality, and anomaly detection are frequently covered in-depth in data science courses.

Database Systems

Although query languages and database administration are typically not as heavily stressed in data analytics courses, they may touch on database systems.

SQL queries, database management principles, and other techniques for effectively retrieving and manipulating data may be covered in data science courses.

Advanced Analytics Platforms

The use of commonplace analytics tools and platforms like Excel, Tableau, or Power BI may be the main focus of data analytics courses.

Advanced analytics tools and frameworks for managing large-scale data processing and machine learning tasks, like Apache Spark or H2O.ai, may be introduced in data science courses.

Optimization Techniques

In comparison to data science courses, data analytics courses typically do not cover optimisation techniques as extensively.

Optimisation strategies like linear programming, integer programming, and restricted optimisation are covered in data science courses and are helpful for solving challenging business challenges.