Topics of the Data Science Course:
1️⃣ Introduction to Data Science:
- Definition of data science and its importance.
- Stages of data science: from data collection to decision-making.
- The differences between data science, data analysis, and artificial intelligence.
2️⃣ Data collection and analysis:
- Types of data (structured and unstructured).
- Data collection techniques from various sources such as databases, the internet, and open data systems.
- Data cleaning and preparation for analysis.
3️⃣ Statistics and Data Analysis:
- Statistical fundamentals for data analysis (mean, distribution, standard deviation).
- Descriptive analysis and inferential analysis.
- Probability distributions and statistical tests.
4️⃣ Data Visualization:
- Graphical visualization tools and techniques such as charts, graphs, and heat maps.
- Using tools like Tableau, Power BI, and Matplotlib to visualize data.
5️⃣ Programming and Data Analysis using Python and R:
- Learn to use Python libraries like Pandas and NumPy for data analysis.
- Applications of R in data science.
- Handling large datasets using tools like Hadoop and Spark.
6️⃣ Machine Learning:
- Introduction to machine learning and its types (supervised and unsupervised learning).
- Machine learning applications such as classification, regression, clustering, and pattern recognition.
- Applications of deep learning using neural networks.
6️⃣ Modeling and Forecasting:
- Building predictive models using historical data.
- Testing and analyzing the models to determine their accuracy.
6️⃣ Ethics of Data Science and Data Protection:
- Understanding issues related to data ethics and user privacy.
- Data protection and privacy protection in analysis processes.
6️⃣ Practical applications in data science:
- The application of data science in various fields such as marketing, healthcare, finance, sports, and others.
- Using data science in making strategic decisions.
Objectives of the Data Science Course:
- Empowering participants to understand data science: Raising participants' awareness on how to collect and analyze data to obtain actionable insights.
- Training participants on data science tools: Teaching the tools and programming languages used in data science such as Python and R.
- Teaching statistical analysis techniques: Training participants on how to use statistical methods to analyze data and draw conclusions.
- Introducing participants to the basics of machine learning: Enabling trainees to understand the applications of artificial intelligence and machine learning in data analysis.
- Enhancing data interpretation and visualization skills: Enabling participants to present complex data in a visually understandable manner.
- Helping participants build predictive models: Teaching participants how to build models to predict future outcomes using historical data.
- Introducing participants to data science ethics: Raising awareness of the importance of data protection and privacy rights in data science.
The target audience for the data science course:
- Data Analysts: those who wish to develop their skills in data analysis and use advanced tools.
- Data Engineers: those who work on building systems that manage data and wish to learn more analytical methods.
- Data Scientists: those who wish to deepen their knowledge in statistical modeling, machine learning, and deep learning.
- IT specialists: those who aspire to expand their skills and transform technical knowledge into data science applications.
- Students and graduates in the fields of computer science and mathematics: those who aspire to enter the field of data science and acquire practical skills.
- Managers and business decision-makers: those who need to use data to extract insights and make informed decisions.
- Anyone interested in learning how to use data to solve problems and achieve professional goals.
The data science course is ideal for individuals who wish to explore and develop their skills in data analysis and its application in various fields.