Data Analytics

About Course

Participants will learn data manipulation, data visualization, statistical analysis, machine learning, and predictive modeling techniques using popular tools and programming languages such as Python, R, and SQL. The course emphasizes hands-on experience with real-world datasets.

Benefits of the course

Gain proficiency in data analytics techniques and tools highly sought after in today's data-driven world.
Learn to extract actionable insights from data to drive business strategies and improve performance.
Enhance problem-solving abilities and critical thinking skills through data analysis.
Prepare for careers in data analysis, business intelligence, data science, and related fields.
Stay competitive in a rapidly evolving job market with high demand for data analytics skills.

Course Content

Role and importance of data analytics in decision-making and business intelligence.
Exploring the data analytics process and its applications in various industries.

Instructor

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(2 Ratings)

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$200

30-Day Money-Back Guarantee

Update: June 9, 2024
Lectures20
Skill LevelExpert
LanguageEnglish
Course Duration: 38h 15m

Requirements

No requirements

Target Audience

Undergraduates
Recent Graduates
Early Career Professionals
Tech Enthusiasts
Mid-Career Professional
Anyone interested in transitioning into the technology industry.

$200 $200

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About Course

Benefits of the course

Course Content

Role and importance of data analytics in decision-making and business intelligence.

Exploring the data analytics process and its applications in various industries.

Techniques for cleaning, transforming, and preparing raw data for analysis.

Handling missing values, outliers, and inconsistencies in data.

Exploring and summarizing data to uncover patterns, trends, and relationships.

Visualization techniques such as histograms, scatter plots, and box plots.

Basic statistical concepts and measures such as mean, median, mode, variance, and standard deviation.

Hypothesis testing, confidence intervals, and correlation analysis.

Creating effective visualizations to communicate insights and findings from data.

Using tools like matplotlib, seaborn, and Tableau to create charts, graphs, and dashboards.

Techniques for building predictive models to forecast future trends and outcomes.

Regression analysis, classification algorithms (e.g., logistic regression, decision trees), and ensemble methods.

Introduction to machine learning algorithms and techniques for pattern recognition and predictive modeling.

Supervised learning, unsupervised learning, and semi-supervised learning approaches.

Exploring methods for discovering patterns and insights from large datasets.

Association rule mining, clustering, and outlier detection techniques.

Big data technologies and platforms (e.g., Hadoop, Spark) for processing and analyzing large volumes of data.

Distributed computing, parallel processing, and scalability considerations.

Ethical considerations and legal implications related to data analytics and data privacy.

Compliance with regulations such as GDPR (General Data Protection Regulation) and HIPAA (Health Insurance Portability and Accountability Act).

Instructor

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Target Audience

Data Analytics

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Data Analytics

About Course

Benefits of the course

Course Content

Introduction to Data Analytics

Role and importance of data analytics in decision-making and business intelligence.

Exploring the data analytics process and its applications in various industries.

Data Wrangling and Preparation

Techniques for cleaning, transforming, and preparing raw data for analysis.

Handling missing values, outliers, and inconsistencies in data.

Exploratory Data Analysis (EDA)

Exploring and summarizing data to uncover patterns, trends, and relationships.

Visualization techniques such as histograms, scatter plots, and box plots.

Statistical Analysis

Basic statistical concepts and measures such as mean, median, mode, variance, and standard deviation.

Hypothesis testing, confidence intervals, and correlation analysis.

Data Visualization

Creating effective visualizations to communicate insights and findings from data.

Using tools like matplotlib, seaborn, and Tableau to create charts, graphs, and dashboards.

Predictive Modeling

Techniques for building predictive models to forecast future trends and outcomes.

Regression analysis, classification algorithms (e.g., logistic regression, decision trees), and ensemble methods.

Machine Learning

Introduction to machine learning algorithms and techniques for pattern recognition and predictive modeling.

Supervised learning, unsupervised learning, and semi-supervised learning approaches.

Data Mining

Exploring methods for discovering patterns and insights from large datasets.

Association rule mining, clustering, and outlier detection techniques.

Big Data Analytics

Big data technologies and platforms (e.g., Hadoop, Spark) for processing and analyzing large volumes of data.

Distributed computing, parallel processing, and scalability considerations.

Data Ethics and Privacy

Ethical considerations and legal implications related to data analytics and data privacy.

Compliance with regulations such as GDPR (General Data Protection Regulation) and HIPAA (Health Insurance Portability and Accountability Act).

Instructor

Requirements

Tags

Target Audience

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