Practical Data Analyst Course Syllabus | IABAC

The Practical Data Analyst Course Syllabus covers data analysis fundamentals, Excel, SQL, Python, data visualization, statistics, machine learning basics, and real-world projects. It equips learners with hands-on skills for data-driven decision-making and business insights.

1. Practical Data
Analyst Course
Syllabus
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2. Overview of Data
Analytics and Its
Importance
Transforming Data into Insights
Data analytics enables organizations to convert
vast amounts of raw data into actionable
insights, facilitating informed decision-making
and strategic planning across various sectors.
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3. 01 02 03
Key Skills Required for Data Analysts
Analytical
Thinking
Technical
Expertise
Effective
Communication
Ability to interpret
complex datasets and
identify trends, enabling
data-driven decision-
making and strategic
insights that support
business objectives.
Proficiency in tools like
SQL, Excel, and
programming languages
such as R and Python,
essential for data
manipulation, analysis,
and visualization tasks.
Skill in presenting data
findings clearly to diverse
audiences, ensuring that
insights are understood
and actionable, fostering
collaboration across
teams.
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4. Tools and Technologies Used in
Data Analytics
Diverse Toolset
for Analysis
Integration of Advanced
Technologies
Data analytics employs a variety of
tools, including spreadsheet software,
statistical analysis programs, and
visualization platforms. Each tool serves
specific functions, enhancing the
analyst's ability to manipulate data,
perform complex analyses, and present
findings effectively to stakeholders
across different sectors.
The integration of machine learning
frameworks and big data technologies is
transforming data analytics. Tools like
TensorFlow and Apache Spark enable
analysts to build predictive models and
process large datasets efficiently,
thereby enhancing the capability to
derive insights and support data-driven
decision-making in organizations.
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5. Methods of Data
Collection
Importance of Method Selection
The choice of data collection methods
significantly impacts the validity and reliability of
research outcomes, necessitating a careful
evaluation of each method's strengths and
limitations in relation to specific research
objectives and contexts.
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6. Data Storage Solutions and Management
Techniques
Importance of Data
Accessibility
Scalability of Storage
Solutions
Data Security Protocols
Ensuring data is easily accessible is
crucial for timely decision-making.
Organizations must implement
efficient retrieval systems to
enhance data usability for analytics
and reporting purposes.
As data volumes grow, scalable
storage solutions are essential.
Organizations should choose
systems that can expand
seamlessly to accommodate
increasing data without
compromising performance.
Implementing robust security
measures is vital to protect
sensitive data. Organizations must
adopt encryption, access controls,
and regular audits to safeguard
against data breaches and
unauthorized access.
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7. Introduction to
Statistical Analysis
Role in Decision-Making
Statistical analysis provides a systematic
approach to interpreting data, enabling
organizations to make evidence-based
decisions by identifying significant patterns and
relationships within datasets, ultimately
enhancing strategic planning and operational
effectiveness.
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8. Overview of Machine
Learning Concepts
Fundamental Principles of
Learning
Machine learning relies on algorithms that
enable systems to learn from data, adapt to new
inputs, and enhance their performance
autonomously, thereby transforming raw data
into valuable insights across various
applications.
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9. Thank you
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