Data science is one of the hottest and fastest-growing fields that almost everyone wants to jump into. By 2024, the **machine learning **market worldwide is anticipated to reach $20.83 billion.To leverage this massive opportunity, it’s the right time to hone your data science skills or learn them by joining a** data science bootcamp in Silicon Valley. **But what capabilities and technical skills should you focus upon when joining such a bootcamp or eyeing a career in data science? Let’s help you to find some answers.

**Must have technical skills and capabilities**

**Data Science Fundamentals along with Statistics**

To become a successful data scientist,you need to have a solid knowledge of the basics of data science, artificial intelligence, and **machine learning **as a whole. Additionally, you should understand the relevant topics in statistics and mathematics like sample and population, standard deviation, probability distributions, skewness and kurtosis, CLT, relational algebra, matrices and linear algebra functions, database basics, binary search tree and hash functions, etc.

**Programming**

Programming gives you a way to communicate with machines. Though you don’t need to be the best in programming, you should surely be comfortable with it to progress in the field of data science. **Python** and R are two of the most used programming languages in data science. **Python** is a general-purpose language that has multiple data science libraries, while R is suited for data visualization and statistical analysis.

**Data Analytics and Manipulation**

**Data analytics** is all about getting the feel of data and making sense of it. For instance, it could involve figuring out which products are most frequently bought by customers, the weekly average sales, etc. Typically, you’ll analyze raw data to spot trends and get insights. For data analysis (a subset of data analytics), you’ll usually use Pandas in **Python**, SQL, or Excel. Before you perform data analysis, you’ll have to manipulate the data by cleaning and transforming it into a format that gets it ready for analysis.

**Data Visualization**

In the domain of **machine learning**, data visualization is an interesting part where you’ll need to construct a story from the visualizations. For this, you’ll have to be familiar with plots like bar charts, histogram, pie charts, and be adept in handling advanced charts like thermometer charts, waterfall charts, etc. In exploratory data analysis, such plots would be helpful while bivariate and univariate analysis would become much easier when you use colorful charts.

**Machine Learning**

This is a core skill to possess if you want to excel in the field of data science. You can begin with a simple logistic and linear regression model and then proceed to advanced ensemble models like XGBoost, Random Forest, etc. **Machine learning** is a subset of artificial intelligence that contributes to data modeling. When you have to handle and operate on huge volumes of data and facilitate a decision-making process based on data, **machine learning** is a must-have skill.

**Other skills**

You’ll also need good and persuasive communication skills, curiosity, storytelling skills, and structured thinking to become a good data scientist.

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