Full Stack Data Analytics Career Path For Banking and Finance

Discover the power of Python for analyzing data and SQL for managing databases. Get ready to start your own data analysis journey with Python's versatile tools and SQL's database querying capabilities.

• 1.1 Python Basics:
  • ‍Understanding Python syntax and data types.
• 1.2 Object-Oriented Programming (OOPs):
  • Implementing classes and objects.
• 1.3 Control Structures and Functions:
  • Using conditional statements and functions.
• 1.4 Data Structures:
  • Manipulating lists, tuples, dictionaries, and sets.
• 1.5 Python Libraries for Data Science:
  • Utilizing Numpy, Pandas, Matplotlib, and Seaborn for data analysis.‍
• 1.6 SQL:
  • ‍Database design, creation, and querying using MySQL Workbench.

Uncover hidden insights in data and automate tasks with ease. Learn how to explore and clean data effectively while automating tasks using shell scripting. Gain practical skills to kickstart your data analysis projects.

• 2.1 Exploratory Data Analysis (EDA):
  • ‍Data sourcing, cleaning, univariate, bivariate, and multivariate analysis.
• 2.2 Unix and Shell Scripting:
  • ‍Basic shell scripting for automating tasks.
• 2.3 Basic Excel:
  • Data manipulation and analysis using Microsoft Excel.

Understand the stories behind the numbers and make informed decisions. Learn essential statistical concepts and hypothesis testing techniques to draw meaningful conclusions from data. Get ready to interpret data with confidence.

• 3.1 Descriptive and Inferential Statistics:
  • Understanding data distributions and making inferences.
• 3.2 Probability:
  • Probability theory and its applications.
• 3.3 Distributions:
  • Discrete and continuous probability distributions.
• 3.4 Central Limit Theorem:
  • Understanding the behavior of sample means.
• 3.5 Linear Algebra and Calculus:
  • Basic concepts for machine learning.
• 3.6 Hypothesis Testing:
  • A/B testing, Chi-squared test, ANOVA.

Harness the power of machine learning to uncover patterns and tell compelling stories with data. Dive into predictive modeling, time series analysis, and data visualization techniques. Equip yourself to create impactful data-driven narratives.

• 4.1 Machine Learning:
  • Supervised and unsupervised learning.
• 4.2 Linear Regression:
  • Predictive modeling for continuous variables.
• 4.3 Logistic Regression:
  • Predicting categorical outcomes.
• 4.4 Clustering:
  • Unsupervised learning for grouping similar data points.
• 4.5 Advanced Machine Learning:
  • Tree models, decision trees, ensembles, random forests.
• 4.6 Time Series Forecasting:
  • Techniques for analyzing and forecasting time series data.
• 4.7 Data Visualization and Storytelling:
  • Communicating insights using Tableau or PowerBI, Excel, and structured problem-solving frameworks.‍
• 4.8 Structured Problem Solving and Frameworks:
  • Business problem-solving methodologies like 5Ws, 5 Whys, Business Model Canvas, Issue Tree Frameworks, and Operations Research using Pyomo.

Enhance your professional toolkit with essential business communication and soft skills. Learn how to communicate effectively and collaborate with others in a business setting. Get ready to excel in your professional journey.

• 5.1 Business Communication:
  • ‍Effective communication in a business context.
• 5.2 Soft Skills:
  • Interpersonal skills crucial for professional success.