From Non-Tech to Data Science: Skills Required for Career Transition

Mathematics: 

For data science practitioners to comprehend machine learning and deep learning ideas and to build a strong analytical mentality, basic mathematics, particularly calculus, and linear algebra, are essential.

Statistics: 

The basis of data science is the study of the collection, organisation, analysis, interpretation, and presentation of numerical data.

Probability:

The main objective of probability is the study of random events. Since the probability theorem is the mathematical foundation of statistical inference and will be helpful when you get to more sophisticated areas of machine learning, it is a great place to start your data science journey.

Some key probability concepts to understand include:

• Events that are independent and dependent
• Theorem of the Counting
• Random variables, both discrete and continuous
• Bayesian calculus
• Theorems of limits

Calculus:

Calculus is the study of length, area, and volume, as well as how quantities change over time. It is an essential part of machine learning and is frequently applied in model optimization.

Linear Algebra:

Data scientists may manipulate data and comprehend machine learning algorithms with the help of linear algebra, which examines equations and matrices.

Python:

Python, the most popular programming language in data science, is easy to learn, has a big community to whom you may turn for help with queries and problems, and provides an incredible set of data science libraries for the vast majority of the work you’ll be asked to complete on the job.

Some of the abilities you must be qualified for include:

• Constructing conditional expressions in Python Using Python loops Python functions
• Structures of data
• DataFrames
• Object-oriented design

SQL:

SQL Structure Query Language (SQL) is a declarative language that you will almost certainly use as a data scientist. It’s the language you’ll need to “communicate” with the database that houses your data. In practice, it allows you to get and extract data from one or more database tables.

The following are the fundamental SQL principles that you must understand:

• For filtering and selection, use the SELECT and WHERE statements.
• For data aggregation, use COUNT, SUM, GROUPBY, MAX, MIN, and HAVING.
• For creating useful unique lists and distinct averages, utilize unique and COUNT DISTINCT.
• LEFT, RIGHT, and INNER JOIN UNION and UNION ALL.
• Time and string conversions
• SELF JOINS
• Sub-queries and their effect on query efficiency

Machine learning:

Machine learning, a branch of mathematics and computer science, use machines to recognize patterns in data, extract insights, and automate real-time decision-making without the involvement of humans. There are two types of machine learning algorithms: supervised and unsupervised.

Model Development and Deployment: 

A machine learning model analyzes a training dataset, allowing the algorithm to create desired outputs via observations, predictions, and continual learning from previous failures.  As a consequence, over time, it reaches a high level of accuracy.

The following are the fundamental supervised algorithms that you should be familiar with:

• K-Nearest neighbors
• Regression linear
• Support Vector Machines (SVMs)
• Decision Trees
• Random Forests are all methods for doing logistic regression.

Unsupervised algorithms

Unsupervised algorithms operate on unlabeled or uncategorized data. The machine learning algorithm identifies patterns and seeks to determine the underlying structure of the dataset without any supervision. This includes grouping the data based on similarities or using the association strategy to find correlations between variables in a large dataset.

Unsupervised learning entails mixing comparable data for segmentation and trend analysis, as well as grouping and dimensionality reduction. Clustering organizes data into groups based on features, whereas dimensionality reduction decreases variables while keeping relevant characteristics.

The unsupervised learning fundamentals you should be well-versed in are:

• K-means clustering
• Density-based spatial clustering of applications with noise (DBSCAN)
• PCA (principal component analysis)
• t-distributed stochastic neighbor embedding (t-SNE)

Wrangling Data :

Data wrangling, also known as data preparation or cleaning, is an important stage in data science since it converts raw, unstructured data into organised analytical forms.

Visualization of Data :

Tableau is a visual analytics platform that empowers people and organizations to make the most of their data, whereas Power BI is a collection of software services, apps, and connectors that work together to transform disparate data sources into coherent, visually immersive, and interactive insights.

Power BI integrates data sources, visualizes and communicates information, and shares critical data with Tableau, with some commonalities and minor variations.

Understanding of popular deep learning frameworks such as PyTorch, TensorFlow, and others in software development. 

To further immerse yourself in the field of data science and master these skills, consider exploring dedicated courses such as the Scaler’s Data Science program. This course offers a well-organized and thorough curriculum, guiding you through a structured learning path to achieve mastery of the complexities of data science.