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Data Science With Python
Learn Data Science With Python to become a Data Scientist & Analyst expert. Enroll yourself now and get a competitive advantage that leads to new career opportunities.
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About Data Science Using Python
Explore the Power of Python for Data Science at Brainalyst
Welcome to Brainalyst, the premier destination for top-notch data science training! Our Python for data science course provides a comprehensive and hands-on learning experience that equips you with the skills and knowledge needed to thrive in the field.
Master Python for Data Science
Our Python data science course covers all the essential topics, empowering you to become proficient in data science using Python. From data analysis and statistical modeling to data visualization and machine learning, our course has got you covered.
Harness the Power of Popular Python Libraries
Learn to leverage popular Python libraries like NumPy, Pandas, Matplotlib, and Scikit-learn. These libraries enable you to manipulate data, conduct statistical analysis, create compelling visualizations, and build powerful machine learning models.
Real-World Projects and Practical Examples
Our experienced instructors guide you through the course with real-world projects and practical examples. This hands-on approach ensures that you gain the confidence and expertise to apply your knowledge in practical scenarios.
Stay Ahead with the Latest Industry Trends
At Brainalyst, we prioritize keeping up with the latest trends and technologies in data science. Our course materials and curriculum are regularly updated to reflect the ever-evolving industry landscape.
Flexible Learning Options
Choose from our flexible learning options to suit your schedule and preferences. Whether you prefer online or in-person classes, we provide the flexibility to learn at your own pace and convenience.
Join a Thriving Community of Students
Join the thriving community of thousands of satisfied students who have benefited from our comprehensive and practical training. Whether you’re an aspiring data scientist or a seasoned professional, our Python for data science course is the perfect choice to enhance your skills and advance your career.
Enroll Today and Unleash the Potential of Python in Data Science
Data Science using Python Curricullum
In this data science training with Python, you will gain knowledge in data handling, visualization, statistical analysis, and predictive modeling. The certification program is delivered by industry experts in both classroom and online training modes. The training emphasizes hands-on projects to help you develop professional-level competency.
- Chapter 1.1 : Basic Excel
- Chapter 1.2 : Basic Programming Elements
- Chapter 1.3 : Introduction of Basic Statistics
- Chapter 1.4 : Overview of Dashboard
- Chapter 1.5 : Business dashboard creations
- Chapter 1.6 : Data manipulation using functions
- Chapter 1.7 : Data Visualization in Excel
- Chapter 1.8 : Introduction to Analytics & Data Science
- Chapter 1.9 : Create dashboard in Excel – Using pivot controls
- Chapter 2.1 : Data Analytics with VBA
- Chapter 2.2: A look at some commonly used code snippets
- Chapter 2.3 : Programming Constructs in VBA
- Chapter 2.4 : Functions & Procedures in VBA-Modularizing your programs
- Chapter 2.5 : Objects & Memory Management in VBA
- Chapter 2.6 : Error Handling
- Chapter 2.7 : Controlling accessibility of your code – Access Specifiers
- Chapter 2.8 : Code reusability – Adding references and components of
your code - Chapter 2.9 : How VBA works with Excel
- Chapter 2.10 : Key Component of programming language
- Chapter 2.11 : Communicating with your users
- Chapter 3.1 : Intro to RDBMS & Basic SQL
- Chapter 3.2 : Data Based Object Creation (DDL commands)
- Chapter 3.3: Data Manipulation
- Chapter 3.4: Accessing data from multiple tables using Select
- Chapter 3.5: Advanced SQL
- Chapter 4.1 : Getting Started
- Chapter 4.2 : Data handlings & Summaries – I
- Chapter 4.3 : Data handlings & Summaries – II
- Chapter 4.4 : Building Advanced Reports/Maps
- Chapter 4.5 : Calculated Fields
- Chapter 4.6 : Table Calculations
- Chapter 4.7 : Parameters
- Chapter 4.8 : Buildings Interactive Dashboard
- Chapter 4.9 : Building Stories
- Chapter 4.10 : Work with data
- Chapter 4.11 : Sharing work with others
- Chapter 5.1 : Introduction to the field of Data Science and Python
- Chapter 5.2 : Data Types and Structures of Core Python
- Chapter 5.3 : Role of Modular Programming in Python
- Chapter 5.4 : Vectorized Data Structures: Numpy Array and Pandas Series
- Chapter 5.5 : Data Mining (Basic)
- Chapter 5.6 : Data Mining (Intermediate)
- Chapter 5.7 : Data Mining (Advanced)
- Chapter 5.8 : Data Quality check
- Chapter 5.9 : Data Visualization
- Chapter 5.10 : Descriptive Statistics
- Chapter 5.11 : Understanding Probability Distribution
- Chapter 5.12 : Hypothesis Testing
- Chapter 5.13 : Finding Business Insights using Statistics
- Chapter 5.14 : Combining Data Mining and Statistics
- Chapter 5.15 : Introduction to Predictive Modeling
- Chapter 5.16 : Encoding and Binning
- Chapter 5.17 : Basics of Regression
- Chapter 5.18 : Regression Model Building : Pre-Modeling
- Chapter 5.19 : Regression Model Building : Modeling
- Chapter 5.20 : Regression Model Building – Post Modeling
- Chapter 5.21 : Basics of Classification using Logistic Regression
- Chapter 5.22 : Classification Model Building : Pre-Modeling & Modeling
- Chapter 5.23 : Classification Model Building : Post Modeling
- Chapter 5.24 : Introduction to Machine Learning
- Chapter 5.25 : Decision Trees
- Chapter 5.26 : K Nearest Neighbour
- Chapter 5.27 : Naive Bayes
- Chapter 5.28 : Support Vector Machines
- Chapter 5.29 : Ensemble Learning
- Chapter 5.30 : Creating a UDF to apply all functions in a single go (pipeline)
Who Should Do?
Candidates from diverse technical or quantitative backgrounds, such as Engineering, Finance, Mathematics, and Statistics, are encouraged to pursue a career in Data Science and Machine Learning. For candidates without a technical background, it is beneficial to have familiarity with basic data analytics tools like Excel, SQL, or Tableau.
To assess the suitability of your profile and explore potential career options, please reach out to our counseling team. They will be more than happy to assist you.
FAQ
Python for data science offers a powerful approach that involves utilizing Python in data analysis, machine learning, and various data-related tasks. Python, renowned for its versatility, user-friendliness, and robust capabilities, has gained widespread adoption within the data science community.
With Python, you gain access to an extensive ecosystem of libraries and frameworks specifically tailored for data science. These include NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, and PyTorch, which provide a flexible and comprehensive platform for data analysis, machine learning, and other data-related activities.
By harnessing the power of Python for data science, you can unlock a multitude of opportunities and derive meaningful insights from your data. Whether you're a novice or an experienced practitioner, Python empowers you to confidently and efficiently tackle complex data challenges.
This Data Science with Python course is the ideal stepping stone in the learning journey as a candidate data scientist. It will provide you with an understanding of data analytics tools and techniques, data analysis, visualization, Python basics for data science and its libraries, web scraping, and natural language processing.
Yes, it is definitely worth learning Python for data science. Python has become one of the most popular programming languages for data science due to its versatility, ease of use, and powerful capabilities. As a result, many data scientists, analysts, and professionals have switched to Python as their preferred language for data analysis, machine learning, and other data-related tasks.
To enroll in the Data Science with Python course, it is recommended that you have a basic understanding of programming concepts such as data types, variables, loops, functions, and control structures. In addition, it would be helpful if you have some experience in Python or any other programming language. However, it is optional to have prior experience in programming to enroll in this course.
Additionally, it is recommended that you have a basic understanding of mathematical concepts such as linear algebra, calculus, and statistics. This will enable you to understand the underlying concepts of data analysis and machine learning.
The Data Science with Python course provides you access to 192 hours of in-depth learning material that you will follow at your speed. It is essential to practice Python programming to gain a firm hold on learning data science with Python; this program will help you quickly.
At Brainalyst, we offer lifetime access to our Data Science with Python course once you enroll. This means you can access the course content, assignments, projects, and resources for as long as needed. In addition, you can go back to the course material and review it anytime you need to refresh your knowledge or skills.
In addition, you will also receive free updates to the course content whenever we make any improvements or add new modules or resources. This ensures that you stay up-to-date with the latest developments in data science and continue to build on your skills.
Course Fee
₹
45,000
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Full Lifetime Access
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Live Interactive Classes
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Certificate of completion
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