Foundation of Data Science -- For Beginners students.

From 37.56 $ /h

Hi Students,

Foundation of Data Science is for complete Beginners.

This course is In-depth knowledge and hands-on tools to use and work with different kinds of data. Gaining practical experience across the data science pipeline by acquiring proficiency in the data science coding language R.

Foundations of Data Science is for those who work with both structured – spreadsheets and data tables – and unstructured data and who need to understand how to automate data to gain insights, contribute to strategic discussions and make data-driven decisions.

With my Course you’ll gain a broad understanding of the tools and techniques used to work with data.

1. What is Data: Learn about data types, structures and frameworks

2. Interacting with Data: Understanding how data is used and how to use data

3. Where Data comes from: The processes for acquiring and sharing Data

4. Storing & Structuring Data: Working with different types of databases and data sets

5. Cleaning Data: Working with messy or incomplete data sets

6. Analyzing Data: Gaining insights with data science techniques and tools

7. Visualizing Data: Best practices and tools for data visualization and presentation

8. Machine Learning & Artificial Intelligence: How data is used by machines

9. Working as a Data Scientist: What do data scientists really do?

10. Data & Society: Ethics and the uses of data in society

Extra information

Laptop is needed :)

Location

Online from Switzerland

About Me

Hi Learners,

I am Noshima and I am a Software engineer working. I work in technology firm and have experience of 5 years. Along with my work since last 6-7 years I am teaching my students through online platform as my hobby and as my passion to share knowledge as much as I can in order to benefit my student.

See you in my CLASS.

Education

Degree - B.Tech in Computer Science.
Graduation Year - 2015
GDPA - 8

Certification in Data Science from EPFL Switzerland.
Year - 2022.2023
Score - Pass with all project submission

Teaching experience of more than 7 years.

Experience / Qualifications

Qualification - Bachelor in Technology.
Experience - more than 5 years

Worked in USA companies Project for more than 3 years.

Age

Children (7-12 years old)

Teenagers (13-17 years old)

Student level

Beginner

Duration

45 minutes

The class is taught in

English

French

Skills

Database

Computer science

Availability of a typical week

(GMT -05:00)

New York

Online via webcam

Mon

Tue

Wed

Thu

Fri

Sat

Sun

00-04

04-08

08-12

12-16

16-20

20-24

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◾ Tools

RStudio • SQL • SPSS • SAS • Jamovi • JASP

◾ Statistical Methods & Tests

Student's t-test • ANOVA • MANOVA • ANCOVA • Regression (linear & logistic) • Correlation • Chi-square • Nonparametric tests • PCA • MCA • Exploratory factor analysis • Classification / Clustering • Mediation • Moderation • Interpretation

◾ Data analysis & decision support

- Data preparation, structuring and validation using SAS, R and SQL
- Descriptive, exploratory and multivariate statistical analyses on business data
- Production of performance indicators and actionable analyses to support decision-making

◾ Selection and implementation of methods

- Preparation and structuring of databases
- Hypothesis testing and univariate, bivariate and multivariate analyses (ANOVA / ANCOVA)
- Linear and logistic regressions
- Factor analyses (PCA / MCA)
- Mediation and moderation models
- Classification / clustering

1) Academic support

- Lectures, tutorials, projects and assignments in statistics
- Help in understanding and interpreting the results
- Preparation for exams and academic presentations

2) Statistical analysis

- Descriptive statistics (univariate and bivariate)
- Multivariate analyses
- Data exploration and outlier detection

3) Statistical tests

- Correlations (Pearson, Spearman, Cohen's Kappa)
- t-tests (one and two samples, independent or paired)
- Chi-square, binomial tests
- z-scores and associated indicators

4) Statistical modeling

- Linear regressions (simple and multiple)
- Logistic regression
- Interpretation of coefficients, diagnostics and validation of models

5) ANOVA & ANCOVA

- One- or multi-factor ANOVA
- Repeated measures ANOVA
- Fixed and random effects
- Post-hoc tests and effect sizes

6) Factor analyses

- ACP / PCA (scree plot, factor scores, matrices)
- Exploratory factor analysis
- Factorial rotations
- Validation and interpretation of structures and clusters

◾ Reporting & communication

- Clear, structured and concise reporting of results
- Visualizations tailored to decision-makers
- Support for strategic and operational decision-making