Learn fundamental statistical words such as mean, median, mode, variance, and standard deviation, which are essential for interpreting and evaluating data.
Gain knowledge about handling data and producing graphs and charts using well-known Python tools like NumPy, Pandas, and Matplotlib.
Online self-learning courses offer autonomy, allowing individuals to learn at their own pace. They provide structured training materials with review exercises to enhance understanding. Utilizing multimedia resources like videos and presentations, learners actively engage with the content. while flexibility enables customization of study schedules. This fosters an environment conducive to effective learning and skill development, accommodating personal commitments.
GoLogica offers a Python Statistics for Data Science Online Training course that covers the basic concepts of statistics in Python, an important tool in data science. This course will benefit both beginner and experienced workers who wish to improve their data analysis skills by combining statistical approaches with Python programming.
Students will get a thorough understanding of basic concepts like as probability distributions, hypothesis testing, and qualitative statistics during the course, all of which are important in making acceptable judgments. The course also covers regression, correlation, and variance analysis, which will help students learn the connections between variables. Students will get actual experience in data processing, visualization, and advanced analytics by utilizing Python libraries such as NumPy, Pandas, and Matplotlib.
One of this classes unique components is its focus on practical, practical applications. Participants will take part in interactive tasks and work on actual assignments, applying statistical approaches to genuine datasets. This training prepares them to face data science difficulties in a variety of industries, including banking, healthcare, marketing, and more.
The Python Statistics for Data Science course is designed for data analysts, data scientists, machine learning engineers, and anybody looking to improve their knowledge of statistics in a based on data environment. Expert teachers will guide students through each session, providing that they get an understanding of how to assess and analyze data in order to make educated decisions.
Enrolling in GoLogica Python Statistics for Data Science Online Training will provide learners with the skills and knowledge they will require to succeed in the dynamic world of data science.
Salary Trends:
According to Simplilearn, The average salary of a Python Statistics for Data Science Training Developer typically ranges from $80k to $140k PA. It depends on factors such as experience, location, and specific job responsibilities.
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Python Statistics for Data Science Curriculum
Introduction to Data Types
Numerical parameters to represent data
Mean
Mode
Median
Sensitivity
Information Gain
Entropy
Statistical parameters to represent data
Uses of probability
Need of probability
Bayesian Inference
Density Concepts
Normal Distribution Curve
Point Estimation
Confidence Margin
Hypothesis Testing
Levels of Hypothesis Testing
Testing the DataParametric Test
Parametric Test Types
Non- Parametric Test
Experimental Designing
A/B testing
Association and Dependence
Causation and Correlation
Covariance
Simpson’s Paradox
Clustering Techniques
Logistic and Regression Techniques
Problem of Collinearity
WOE and IV
Residual Analysis
Heteroscedasticity
Homoscedasticity
The GoLogica certification is widely acknowledged, enhancing the credibility of your resume and opening doors to high-level positions in leading multinational corporations globally.
At the end of this course, you will receive a course completion certificate which certifies that you have successfully completed GoLogica training in Python Statistics for Data Science technology.
You will get certified in Python Statistics for Data Science by clearing the online examination with a minimum score of 70%.
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Python Statistics for Data Science Objectives
The course plans to teach statistical ideas and techniques in Python, with a special focus on data analysis, hypothesis testing, regression, and other key methods for collecting insights from data.
Statistics is essential in data science when analyzing data, identifying trends, generating predictions, and testing models. It includes tools for summarizing huge datasets and drawing conclusions from them.
Python has become popular for statistical analysis because to its robust libraries, such StatsModels, NumPy, Pandas, and SciPy.
The most common Python libraries for statistics include:
• Pandas: For data manipulation.
• NumPy: For numerical operations.
• SciPy: For advanced mathematical functions.
• StatsModels: For statistical modeling.
• Matplotlib & Seaborn: For data visualization.
The course includes:
• Descriptive statistics
• Probability theory
• Hypothesis testing
• Regression analysis
• Correlation and causation
• Data visualization techniques
• Probability distributions
• Statistical modeling using Python
Mean, median, mode, standard deviation, and variance are statistical measures used to describe and define a dataset's basic features. They help in understanding data structure and variability.
A sample is a subset of the population, whereas the population consists of all members of a specific group. Statistics uses samples to derive conclusions about the population.
Hypothesis testing is a method to determine if significant statistics support a certain assumption (hypothesis) about a dataset. To make a decision, first generate null and alternative hypotheses, then use tests like chi-square or t-tests.
Correlation analyzes the link between two variables. In Python, correlation may be determined using Pandas' corr() method. The value ranges from -1 to 1, where -1 shows a negative relationship, 1 shows a positive link, and 0 shows no correlation
Overfitting happens when a model is overly complicated, performing well on training data but seriously on unknown data. Cross-validation, regularization, and model complexity reduction strategies can all help stop it.
Probability distributions explain how data points are distributed throughout a range of values. Normal, binomial, and Poisson distributions are among the most common. They help with understanding data behavior and developing predictions.
P-values, ranges of trust, and hypothesis testing are used to assess statistical significance. A p-value less than 0.05 signals statistical significance.
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Visit GoLogica website, locate the 'Certificates' section, follow the instructions to verify your course completion by completing the exam and Get more than 70% marks. And download your certificate.
I'll guide you through the certification process step-by-step, ensuring you're well-prepared and confident in your subject matter by clearing an exam.
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Our mock interviews process involves practice sessions, feedback, and role-playing to enhance candidates' communication skills and confidence in a concise, single-line summary:
"Practice + Feedback = Confident Interview Readiness."
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Our trainers are highly experienced in respective Field and implementing real-time solutions on different Scenarios and Expert in their professionals.
We record each LIVE class session you undergo through this training and recordings of each session class will be updated in your Cloud.
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GoLogica have a 10+ year’s good track record in the training market. However, it was founded in 2013.
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Self-paced training allows learners to study at their own speed, while Live Online training offers real-time, interactive sessions with an instructor.
Self-paced learning offers flexibility, personalized progress, and the ability to review materials at your own convenience.
Live online training offers real-time interaction, immediate feedback, and networking opportunities, which self-paced learning lacks.
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Timetable flexibility depends on the institution and availability; inquire for options.
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Practice consistently, apply learned skills in real-life scenarios, and seek feedback for improvement.
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Self-paced videos can be classified into beginner, intermediate, advanced, and expert levels.
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Our trainers are highly experienced in on specific subject matter to teach and uses by real-time solutions on different Scenarios and Expert in their professionals.
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Python Statistics for Data Science rated
(5.0 / 5) based on 1 reviews.
Triveni
Recently, I finished my Python Statistics for Data Science course at GoLogica. The course taught important statistical principles and Python skills for effective data analysis, interpretation, and visualization.
