What are the Topics Covered in Data Science?
Data science is a multi-disciplinary course that applies advanced analytics techniques and scientific principles to extract valuable and essential information from data for business decision-making, strategic planning, and other uses.
The data science syllabus mainly includes the following topics
- Statistics
- Linear Algebra
- Programming
- Machine learning
- Data Mining
- Data visualization
Topic 1 : Statistics
Statistics deals with collecting, analyzing, interpreting, and presenting masses of numerical data. It is a fascinating topic in the data science field. There are several ways that statistics manifests itself in the practice of data science. These procedures can be beneficial for interpreting data and producing exciting results and predictions. Statistics is one of the most important and interesting data science topics for presentation.
How Important is Statistics in the Field of Data Science?
Things get a little hazy, and opinions start to differ. One can divide the statistics into new and old groups to answer this question more precisely.
Old statistics like regression and hypothesis tests are inherently simplistic. While they can be helpful, many prominent data scientists predict that they will be used less and less. These topics are engaging in data science and will likely become less important as one learns more and statistical techniques evolve. On the other hand, new statistics such as decision trees and predictive power are beneficial and often used by scientists. Most Data Scientists continuously invest more in data preprocessing. This requires a good knowledge of statistics. A few general steps must always be taken to process any data.
- Finding the relationship between elements to eliminate the possibility of duplicate pieces.
- Converting functions to the required format.
- Data normalization and scaling. This step also includes identifying the distribution of the data and the nature of the data.
- Taking over the data for further processing with the required modifications in the data.
- After processing the data, identify the correct approach/model.
- Once the predictions are obtained, the results are verified against various accuracy measures.
- When processing data from the beginning to the end of the entire cycle, there is a requirement for statistics at every single step. This is why a good statistician can also be a good data scientist.
Topic 2 : Linear Algebra
Linear algebra is the foundation of data science and machine learning. Starting their data science journey, established practitioners need to develop a strong understanding of the basic concepts of linear algebra.
By Benjamin Obi Tayo, Ph.D., KDnuggets in Data Science Education, Data Visualization, Linear Algebra, Linear Regression, Mathematics, Python
It is an instrument in data science and machine learning. Linear algebra is the most essential mathematical skill in machine learning. Most of the ML models can be shown in matrix form. The dataset also can be expressed as a matrix.
Here is the linear algebra syllabus for data science:
- Vectors
- Matrices
- Transpose of a matrix
- The inverse of a matrix
- Determinant of a matrix
- Trace of a matrix
- Dot product
- Eigenvalues
- Eigenvectors
Topic 3 : Programming
Programming languages ??for data science are among the popular trends these days. Languages ??like Python, R, and SQL are the mainstays for most data scientists, leading them into analytical roles, while others are useful for careers in fields like data systems development. These are the most in-demand programming languages for the data science field. Python syllabus for Data science includes the following –
- Basics of Python language
- Scientific libraries in Python – NumPy, SciPy, Matplotlib, and Pandas
- Data Visualization
- Scikit-learn and Machine Learning
Topic 4: SQL
Data Science is the study and analysis of data. To analyze the data, we need to extract it from the database. This is where SQL comes into play. Many database platforms are modeled after SQL. It has become the standard for many database systems. In fact, modern extensive data systems like Hadoop and Spark use SQL to maintain relational database systems and process structured data.
To experiment with data by creating testbeds, data scientists use SQL as their standard tool, and to perform data analysis on data stored in relational databases such as Oracle, Microsoft SQL, and MySQL, we need SQL.
SQL syllabus for data science includes the following –
- Relational Database Basics
- Selecting, Inserting, Updating
- Creating, Dropping, Deleting
- Views and Joins
- SQL Integration with Python
Topic 5: Machine Learning
Machine Learning allows computers to learn without being explicitly programmed. —Arthur Samuel, 1959. For any business, industry, and organization that can run data as the primary record or lifeblood, and along with the development, the demand and importance also grow.
Using this technology, you can analyze large amounts of data and instantly calculate risk factors. Machine learning has changed how data engineering is done in data manipulation, extraction, and interpretation. Data science is all about finding information from under processed data. This is done by exploring data at a very unpurified level and understanding its behaviors and trends. This is where machine learning comes into play.
Machine learning for data science involves:
- Data Collection: It is the primary step, and it is essential to collect relevant and reliable data that impact the outcomes.
- Data Preparation: Data preparation is data cleaning. It is required for preparing the data. This ensures that data is error-free and corrupt data point-free.
- Model Training: In this, learning of data starts. Here one uses a training set to predict the result of the output data value. One must repeat this model step training and do it repeatedly to get more accurate results.
- Data Testing: Once completing the above steps, one can evaluate. The model will perform in real-life applications by evaluating the data set.
- Predictions: Once training and evaluation of the model are made, it does not mean that the dataset is perfect and ready to be deployed. One has to improve it by tuning.
Topic 5 : Data Mining
Data mining is used for exploring data to extract meaningful information. It is a vital part of successful analytics initiatives in organizations. The information it generates can be used in business intelligence (BI) and advanced analytics applications, including historical data analysis and real-time analytics applications that examine streaming data as it is generated or collected.
Practical data mining helps in business strategy planning and operations management. This includes customer-facing functions such as marketing, advertising, sales, customer support, manufacturing, supply chain management, finance, and HR. It also plays a vital role in healthcare, government, scientific research, mathematics, sports, and more.
Topic 6: Data Visualization
Data visualization is data representation using graphics, such as charts, plots, infographics, and even animations. It is also an interesting topic in data science. It enables us to see data and analytics presented visually, so they can identify valuable patterns or trends. In big data, data visualization tools and technologies play a vital role in analyzing vast amounts of information and making data-driven decisions. Common general types of data
visualization:
- Charts
- Tables
- Graphs
- Maps
- Infographics
- Dashboards
Used of Data Visualization in Data science
Data visualization skills are very much useful for data scientists. Being able to effectively communicate your data through images rather than words makes your message much more understandable and, in turn, gives you a better chance of impacting your work. So, Data science is a complex field involving many topics.
Significance In Data science
Machine Learning:
An excessive number of machine learning tactics are related to aspects of linear algebra. There is primary component analysis, eigenvalues, and regression, to name a few. This is primarily true when you start working with high-dimensional data, as it tends to involve matrices.
Modeling:
If you want to model behavior, you'll probably use a matrix to subdivide your samples to get accurate results. This act requires you to use general matrix math, including inversion, differentiation, and others.
Optimization:
Understanding the several versions of least squares is helpful in any data scientist job. It is used for dimensionality reduction, clustering, and more. All of these play a part in optimizing networks or projections.
Final Thoughts
We hope this blog has given you ample information regarding what are the important topics we usually cover under the Data Science regime. It's also important to learn these topics so as to grasp a clear understanding of how data science works and what are the various elements to it. In case you want to know about Data Science in detail, you can enroll for the courses offered by The IoT Academy. With guidance from industry experts you can surely pursue your dream of becoming a data scientist in the future.
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