Free Statistics For Data Science Courses Online

Statistics for Data Science

Data Science is a new discipline powered by the essentials of computer science, mathematics, research, as well as applied sciences. Mathematics or specifically statistics has been quite a game changer for data science as it offers methodologies and tools to find, sort, clean, analyze and find structure in data. Statistics is a branch of mathematical science, focusing on working with numerical data and information and transforming them into insights.

Statistics for data science involves –

Data collection from the selected sample, from surveys, social networks, big data, business intelligence, among others
Data Processing, which includes its cleaning, filtering, homogenization, etc.
Data exploration and presentation, especially graphically
Data analysis to draw conclusions that are generally valid from the sample data
Data interpretation to dete

Statistics for Data Science

Statistics for data science involves –

Data collection from the selected sample, from surveys, social networks, big data, business intelligence, among others
Data Processing, which includes its cleaning, filtering, homogenization, etc.
Data exploration and presentation, especially graphically
Data analysis to draw conclusions that are generally valid from the sample data
Data interpretation to detect trends and patterns and predict future scenarios
Calculating probability distribution and estimation
Facilitating understanding of distributions in model-based data analytics
Facilitating statistical modeling for predictions

Techniques - Statistics for Data Science

Arithmetic Mean - It involves dividing the sum of the elements of a set by the number of values in the set

Graphic display – It uses histograms, pie charts, bars, etc.

Correlation – This technique measures if there is a relationship between different variables

Regression – Regression enables to identify if the evolution of one variable affects others

Time series – Used to predict future values â€‹â€‹by analyzing sequences of past values

Data mining and other Big Data techniques – Used to process large volumes of data

Sentiment analysis – Sentiment analysis is the process of using natural language processing, text analysis, and statistics to analyze customer sentiment, often using data from social networks

Semantic analysis – This process helps to extract knowledge from large amounts of texts

A / B testing – It is a popular technique to determine which of two variables works best with randomized experiments

What skills do I need to excel in Python for Data Science?

Technical Skills

Fundamentals of statistics
Descriptive Statistics
Exploratory Data Analysis
Percentiles and Outliers
Probability Theory
Bayes Theorem
Random Variables
Cumulative Distribution function (CDF)
Skewness
Data science programming languages such as R, Java, SQL, and Python, etc.
Working experience with Machine Learning, Multivariable Calculus & Linear Algebra
Ability to write codes and manage big data chunks
Hands-on working experience with real-time data and cloud computing
Ability to use automated tools and open-source software

Soft Skills

Analytical thinking
Business sense
Communication skills
Intellectual curiosity
Interpretive skills
Proactive problem solving
Self-management, including planning and meeting deadlines
Ability to approach issues from multiple perspectives

Technical Tools

Microsoft Excel
Python
R
MATLAB
Tableau
Minitab
Stata
Google Data Studio

Who should take up the Statistics for Data Science certification?

Statisticians, data analysts, data scientists, data engineers, data miners, data architects, robotics professionals, gaming, computation and educational professionals, among others can learn Statistics for Data Science. Marketing analytics professionals who want to use regression and A/B testing techniques can also use the same.

Eligibility & Prerequisites to Learn Statistics

To learn statistics for data science, you would need a minimum of a graduate degree in Mathematics or Statistics. Many statisticians opt for a master's or doctorate degree in subjects such as applied statistics, statistical programming, or analysis of models of variance. If you are already working in a data or technology related job and want to become a statistician, you can pursue a degree in engineering or physics, or you can move a step further and go for a degree in data science.

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