You want to succeed in data science. What are the most important statistics skills you need?

Statistics is a common language, as mathematics for scientific disciplines. It's an objective tool to make data-driven choices. One of the main applications are within data science and data analytics fields.

1. Descriptive Stats: Summarize data. 2. Inferential Stats: Make predictions. 3. Probability Theory: Understand likelihood. 4. Regression Analysis: Model relationships. 5. Statistical Testing: Assess differences. 6. Experimental Design: Minimize bias. 7. Time Series Analysis: Forecast trends. 8. Multivariate Stats: Analyze multiple variables. 9. Software Proficiency: R, Python, MATLAB. 10. Critical Thinking: Interpret findings accurately.

To excel in data science, key statistics skills include: -Descriptive Statistics: Summarizing data using measures like mean, median, mode, variance, and standard deviation. -Inferential Statistics: Making predictions and drawing conclusions from data samples through hypothesis testing and confidence intervals. -Probability Theory: Understanding the likelihood of events occurring and their implications in data analysis. Regression Analysis: Modeling relationships between variables to understand patterns and make predictions. -Experimental Design: Planning and executing experiments to collect data effectively while controlling for variables.

Let me start by saying that Statistics is Everywhere. E-commerce, Weather forecasts, Emergency Preparedness, Political Campaigns, Sports Analytics, Research, Quality Testing, Insurance, Consumer Goods and many more.....name any field, the maximum probability is that the field is related to statistics. Statistics is inseparable from our lives, believe it or not, we use statistics in all walks of our lives consciously or subconsciously. The must-have skills are Probability, Sampling and sampling Techniques, Hypothesis, single-multivariate analysis

To succeed in data science, you need to understand probability, hypothesis testing, and regression analysis. You should also be familiar with different statistical models and machine learning algorithms. Knowing how to visualize data and having strong programming skills in language like Python is crucial too. These skills help you analyze data to find important information and make predictions.

To succeed in data science, there are several key statistics skills that are essential. Here are some of the most important statistics skills you should focus on developing: Descriptive Statistics Inferential Statistics Probability Theory Statistical Modeling Experimental Design Statistical Testing Data Visualization Statistical Programming Bayesian Statistics Machine Learning By honing these statistics skills, you will be better equipped to analyze data, extract meaningful insights, and make informed decisions in your data science projects. Continuous practice and application of these skills in real-world scenarios will help you further enhance your statistical expertise in the field of data science.

Although is attempting to jump into complex technical concepts/tools knowing the basics is crucial and many professionals are lacking that. Most business problems involve predict or classify something and knowing regression and multivariate analysis (clustering) will help you to solve 90% of problems. Descriptive analysis nowadays is mandatory almost like excel to any professional. It’s important to keep in mind when we talk about business problems how simplest the solution better, will be easier to explain and apply.

While it's always a good idea to start with a fundamental introductory statistics course, I learned applied statistics using SPSS case studies - tutorials that go with any SPSS installation. This way you learn through real-world examples. XLStat and Stata packages have excellent tutorials, too.

One of the best ways to practice statistics is to replicate research papers that are accompanied by datasets and replication packages (e.g., American Economic Review in the case of economics papers). This way you will learn from the best researchers in the field.

The tidymodels ecosystem in R is maintained by amazing professionals. I strongly recommend visiting the ecosystem's website and checking out its packages and learning resources. Emil Hvitfeldt, Max Kuhn, Julia Silge, and Vincent Arel-Bundock are among some of the most valuable statistics and R professionals you can follow.