How to Become a Data Scientist in 2023
Data science has become a trendy, in-demand field, but it can take time to figure out how to break into it and get the most bang for your buck regarding education and certification options. To assist you, we’ve put together this manual to decide what certification you need and how to get it.
Plus, we discuss the benefits and drawbacks of purchasing credentials versus learning on your own so that you may decide on your course of action in the best way feasible for your data science career.
Who is a data scientist?
A data scientist has the skills and knowledge to find insights from data. They can apply statistics, math, and data visualisation for their company’s benefit.
Programming languages and business intelligence tools are optional for success. A data scientist may also be involved in machine learning or artificial intelligence research.
How long does it take to be a data scientist?
The time it takes to become a data scientist varies depending on your skills and experience. Generally, the more work, The more experience you have, the quicker it will be. To become a data scientist.
For example, it might take only six months if you’re an experienced programmer who knows how to use Python and SQL.
However, if you don’t learn any programming languages or statistics or have no work experience in data science-related fields, it might take up to two years!
Is it hard to become a data scientist?
It’s simpler than you think to become a data scientist might think.
Several distinct paths can lead to becoming a data scientist, and the process can be broken down into two stages:
- Preparation and 2. training.
The first stage of becoming a data scientist is preparation – you need basic computer science, statistics and math skills.
You also need to be familiar with programming languages such as Python and R.
Finally; you should know SQL very well (Structured Query Language). These skills will set you up for success if you want to become an entry-level data scientist or analyst.
Next comes training – this stage is about gaining the knowledge necessary for your desired role in the field of data science.
Best Courses to learn to become a Data Scientist
We’ve rounded up some of the most highly-reviewed courses you can enroll in to accelerate your journey toward becoming a Data Scientist.
- Fill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlow
- Impress interviewers by showing an understanding of the data science field
- Learn how to pre-process data
- Understand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)
- Start coding in Python and learn how to use it for statistical analysis
- Perform linear and logistic regressions in Python
- Carry out cluster and factor analysis
- Be able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learn
- Apply your skills to real-life business cases
- Use state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big data
- Unfold the power of deep neural networks
- Improve Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross-validation, testing, and how hyperparameters could improve performance
In this course, you will everything there is to know about using Python as a Data Scientist.
- Use Python for Data Science and Machine Learning
- Use Spark for Big Data Analysis
- Implement Machine Learning Algorithms
- Learn to use NumPy for Numerical Data
- Learn to use Pandas for Data Analysis
- Learn to use Matplotlib for Python Plotting
- Learn to use Seaborn for statistical plots
- Use Plotly for interactive dynamic visualizations
- Use SciKit-Learn for Machine Learning Tasks
- K-Means Clustering
- Logistic Regression
- Linear Regression
- Random Forest and Decision Trees
- Natural Language Processing and Spam Filters
- Neural Networks
- Support Vector Machines
This is a great course that covers:
- Successfully perform all steps in a complex Data Science project
- Create Basic Tableau Visualisations
- Perform Data Mining in Tableau
- Understand how to apply the Chi-Squared statistical test
- Use Backward Elimination, Forward Selection, and Bidirectional Elimination methods to create statistical models
- Create a Logistic Regression
- Operate with False Positives and False Negatives and know the difference
- Read a Confusion Matrix
- Create a Robust Geodemographic Segmentation Model
- Transform independent variables for modelling purposes
- Derive new independent variables for modelling purposes
- Check for multicollinearity using VIF and the correlation matrix
- Understand the intuition of multicollinearity
- Create Scripts in SQL
- Present Data Science projects to stakeholders
How can I start my career as a data scientist?
There are many ways for you to start your career as a data scientist. You can go to a school or Institute and take courses like data analytics or data mining.
Another option is taking free online techniques, such as those offered by Coursera and Udemy.
There are also many other resources available on the internet, such as Khan Academy and edX.
In addition, some books may be helpful, including Data Science from Scratch:
Is Python enough for data science?
Python is a programming language commonly used for data science, but there are others.
Python has some benefits, such as being easier to read and learn for beginners.
You can do data science in Python without knowing any other languages.
However, many people find that knowing multiple languages helps them because they can use different tools depending on the situation.
For example, if you want to use machine learning techniques like decision trees or neural networks, you’ll need to know how these algorithms are coded in another language like C++ or Java.
Which degree is best for a data scientist?
To become a data scientist, you must understand statistics and data analysis.
An undergraduate degree in Mathematics, Economics, Computer Science or Physics is usually required.
Graduate degrees in these fields can be more helpful but are optional.
The most important qualification is the ability to code in at least one programming language, such as Python, Java, C++ or R.
Can I learn data science in 1 month?
This question can be tricky, and the answer could be more straightforward.
Many things need to be considered, such as your background, goals, and your goals for using data science.
Most people agree that it would take about six months of dedicated study if you’re going to become an applied statistician or data scientist for a company.
On the other hand, if you use some of the techniques in your work, it might only take one month or less.