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Data Science Training in Hyderabad
Statistics plays a crucial role in Data Science, helping professionals analyze data, extract meaningful insights, and make data-driven decisions. Whether you are a beginner or an experienced professional, mastering statistics is essential for building predictive models and understanding patterns in data. If you are looking for the Best Online Data Science Training in Hyderabad, Coding Masters provides an in-depth curriculum covering all key statistical concepts with hands-on implementation in Python.
Statistics is a branch of mathematics that deals with collecting, analyzing, interpreting, and presenting data. It provides the foundation for machine learning algorithms and data analysis techniques used in Data Science. Understanding statistics helps data scientists make informed decisions based on data trends.
Types of Statistics
Statistics is broadly categorized into two types:
- Descriptive Statistics – Summarizes and describes the main features of a dataset, such as mean, median, mode, and standard deviation.
- Inferential Statistics – Uses sample data to make predictions or generalizations about a larger population through hypothesis testing and confidence intervals.
Population & Sample
- Population: The complete set of data or subjects under study.
- Sample: A subset of the population used to infer insights about the entire population.
Selecting the right sample size is critical for ensuring accurate results and avoiding biases in Data Science applications.
Central Tendencies: Mean, Median, and Mode
Central tendency measures help identify the central value of a dataset:
- Mean: The average of all values.
- Median: The middle value when data is arranged in ascending order.
- Mode: The most frequently occurring value in a dataset.
Understanding these concepts is vital for summarizing data effectively.
Percentiles & Dispersion
- Percentiles help understand the distribution of data, useful in ranking and scoring.
- Dispersion measures (Range, Variance, and Standard Deviation) determine how spread out the data is.
Statistics Implementation with Python – I
Python is a powerful tool for statistical analysis in Data Science. Popular libraries include:
- NumPy for numerical computations
- Pandas for data manipulation
- Matplotlib & Seaborn for visualization
- SciPy & Statsmodels for statistical functions
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