Data Science with Python

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Data Science with Python

Learn Data Science concepts like Machine Learning, Data Analysis, Data Visualization, and Deployment of ML models.

  • Expert-led interactive sessions.
  • 60+ hours of dedicated learning.
  • Key projects and Certifications.
  • WhatsApp Support Group.


LI-MAT Soft Solutions’ Python data science course will help you conquer essential Python-programming concepts such as data and file operations and various Python libraries such as Pandas, NumPy, and Matplotlib, which are critical for Data-Science. Our course is well-suited for beginners and professionals and aligns with industry standards. This Python for Data Science certification training will also help you understand Recommendation Systems, Machine Learning, and many more Data Science concepts to help you shine in your Data Science career and ace your interviews.

This course provides a complete overview of Python's data analytics tools and techniques. Following a blended learning approach, you can learn data science and concepts like data wrangling, mathematical computing, Data Analysis, Machine Learning, Data Visualization, Web Scraping, & NLP. You will master the crucial Data Science tools using Python upon course completion.


Anyone interested in the Data Science domain may register. Understanding basic programming concepts will give you the upper hand. However, if you need to catch up, there is a python refresher to get you started.

Key Takeaways:
  • Expert-Led Live Interactive Sessions.
  • 60+ hours of dedicated learning.
  • Regular Assignments.
  • Assessment (Quiz/Test).
  • WhatsApp Support Groups.
  • Class Recordings.
  • Internship Grade projects and certification.
Career Opportunities:
  • Data Scientist
  • Machine Learning Engineer
  • Data Analyst
  • Data Engineer
  • Business Analyst
  • Data Modeler
  • Data Architect
  • Statistician
  • Quantitative analyst
  • Computer & information research scientist
  • Course Outcome:

    Upon completion of this Data science with python course, you will be able to:

    • Programmatically download and analyze data.
    • Acquire skills in various data kinds, including ordinal, categorical, and encoded data.
    • Acquire data visualization skills.
    • Become an expert at presenting step-by-step data analysis using I Python notebooks.
    • Understand the "Roles" that a Machine Learning Engineer plays.
    • Explanation of Machine Learning.
    • Work with real-life data.
    • Acquire knowledge of predictive modelling tools and methods.
    • Examine machine learning algorithms and how they work.
    • Verify algorithms for machine learning.
    • Carry out sentiment analysis and text mining.
    • Develop business skills while pursuing current goals.

    Why learn Data Science with Python?

    One of the most widely used languages in data science for data analysis, manipulation, and visualization is unquestionably Python. Its accessibility to numerous Data Science libraries makes it the ideal language for creating apps and putting algorithms into practice.

    • According to Glassdoor estimates, the average annual pay for a data scientist in India is $10,00,000. In the Bangalore Area, the typical income for a data scientist is $11,000,000 annually. The supplemental cash salary for a data scientist in the Bangalore area ranges from 58,000 to 10,52,000, with an average of 2,00,000.
    • According to the U.S. Bureau of Labor Statistics, the average annual pay for a data scientist is $119,563.
    • By 2026, there will be around 11.5 million new employment for Data Science specialists, according to the U.S. Bureau of Labor Statistics.
    • There is a tremendous amount of info present. Nevertheless, more resources are required to transform this data into profitable products. In other words, there aren't enough people with the essential skills to help firms take advantage of the promise that data represents. As a result, there is a lack of data scientists.
    Skills Covered:
    • Data Science Toolkit
    • Statistical Modelling
    • Exploratory Data Analysis
    • Data Visualization
  • Data Cleaning
  • Supervised and Unsupervised learning
  • Deployment
  • LI-MAT Soft Solutions Data Science with Python Training Description

    Who should enrol in this online Data Science with Python course?

    The following professions would benefit from the course offered by LI-MAT Soft Solutions:

    1. Architects, technical leads, programmers, and developers.
    2. Freshers who want to become experts in Data Science.
    3. Programmers that want to become "Data Scientists."
    4. Managers of a group of analysts that use analytics.
    5. Business Analysts interested in learning about Machine Learning (ML) Techniques.
    6. Information architects that are interested in developing their predictive analytics skills.
    7. Experts who desire to create automated forecasting models.
    Which career options are most frequent for people who complete this Data Science with Python training?

    You will receive a certificate and be qualified to apply for junior or associate data scientist employment as soon as you have finished this Data Science with Python course. You can become a Senior Data Scientist or Machine Learning Engineer after getting some work experience. With our online course, you have the chance to speak with knowledgeable, professional teachers that can help you navigate the career path of your choice.

    Can I enrol in the Data Science with Python certification course without prior coding experience?

    Prior coding experience is optional to enrol in this Python for Data Science course. We explore the fundamentals of Python coding in the first few introductory modules of the course. You don't need to be familiar with data science or machine learning. This course includes all pertinent topics entirely from scratch.

    Is it possible to become a Data Scientist using only Python?

    Python can be used by itself to apply data science in some situations. Still, sadly for the corporate sector, this is only one piece of the solution for businesses to manage an enormous amount of data. Python is an exciting programming language for people who desire to work as data scientists. Its importance in data science should be taken into account and acknowledged.

    What are data scientists' average salaries like in different nations?

    A compensation survey conducted by Payscale, Glassdoor, and revealed the following wage ranges for data scientists in several nations:

    Average Salary for Data Scientists in Various Countries

    • US: $74439 per year
    • India: RS 10,00,000 per year
    • Australian Dollars 115,000 annually
    • Canada: 79858 C$ annually
    • £52,052 a Year in the UK
    • Annual cost in Singapore: S$70932
    • UAE: 181776 AED annually
    How does Python apply to data science?

    Data science will help us extract information from the data around us. Python can be used to implement various uses for data science. Python is a general-purpose programming language that can create scripts, backend APIs, and webpages. The built-in libraries, frameworks, and tools of Python can be used to carry out a variety of data science tasks.

    Which businesses employ promising Data Science experts?

    Today, data science has advanced significantly. Businesses in practically every sector are attempting to build a data science team to assist them in using their data to develop the business.

    Here is a list of reputable businesses now looking to hire data scientists.

    • Sigmoid
    • Mindtree
    • LinkedIn
    • Paypal
    • 7Oracle
    • EY
    • SAP
    • TCS
    • ZIGRAM
    Data Science Course Fees

    The fees for data science courses fees can vary depending on factors such as the institution, course duration, and level of specialization. Looking for an affordable and comprehensive data science course? Our data science course fees are designed with you in mind! Unlock the potential of big data and analytics without breaking the bank. Enroll now and gain valuable insights into the world of data science, all at an unbeatable price. Take advantage of this opportunity!

    Course Curriculum

    What will we cover in the upcoming months?

    1. Introduction To Data Science
      1. What is Data Science
      2. Task of Data Scientists
      3. Data Science Applications
      4. Aspects and Tools for Data Science
      5. Introduction to Jupyter Notebooks and Spyder IDE
    2. Steps Regarding Data Science
      1. Setting Up an Environment
      2. Collection of Data
      3. Understanding the problem statement
      4. Data Cleaning
      5. Data Manipulation
      6. Data Modelling
      7. Evaluation
    3. Python Refresher
      1. Basic Syntax
      2. Lists
      3. Tuples
      4. Dictionaries
      5. Lambda Functions
      6. Map, Filter Reduce
    4. Necessary Modules
      1. Pandas for Data Analysis
      2. NumPy for Computational Power
      3. SciPy for statistical methods
      4. Matplotlib for custom visualization
      5. Seaborn for express Visualizations
    5. Data Wrangling
      1. Pre-processing Data
      2. Dealing with missing values
      3. B-Fill and F-Fill
      4. Data Formatting
      5. Data Normalization
      6. Data Binning
    6. Exploratory Data Analysis
      1. 1Introduction to EDA
      2. 2Descriptive Statistics
      3. 3Group By
      4. Correlation
        1. Pearson Correlation Coefficient
        2. Spearman Correlation Coefficient
      5. Distance
        1. 1Euclidean Distance
        2. 2Manhattan Distance
      6. Steps Involving EDA
      7. Understanding Visuals
      8. Imputation
      9. Types of Encoding and Encoding Techniques
        1. Introduction to encoding
        2. One Hot Encoding
        3. Label Encoding
        4. One Hot vs Label Encoder
      10. Feature Scaling
      11. Need for feature scaling
      12. Handling Categorical Data
      13. Handling Ordinal Data
      14. Standardization
      15. Types of Scaling and uses
      16. Handling Imbalanced Data
      17. SMOTE Analysis
      18. Up sampling &Under sampling
      19. Feature Transformation
    7. Machine Learning
      1. Introduction to Machine Learning
      2. Machine Learning in Data Science
      3. Machine Learning Algorithms
        1. Supervised Learning
          1. Regression
            1. Simple Linear Regression
            2. Multiple Linear Regression
            3. Non-Linear Regression
          2. Classification
            1. K-Nearest Neighbours
            2. Support Vector Machines
            3. Logistic Regression
            4. Decision Trees with Gini/entropy
            5. Bagging Algorithms
              1. Random Forest
        2. Unsupervised Learning
          1. Clustering
            1. K-means Clustering
            2. Hierarchical Clustering
      4. Evaluation Metrics
        1. OLS (ordinary least squares)
        2. RMSE (Root Mean Squared Error)
        3. MSE (Mean Squared Error)
        4. Accuracy Score
        5. Confusion Matrix
        6. F1 score
        7. Precision
        8. Recall
      5. Flask for Deployment
        1. 1Introduction to Flask Framework
        2. 2Routes
        3. 3GET and POST
        4. 4Run an app
        5. 5Introduction to Deployment
        6. Heroku
        7. 7Deployment on WEB
    8. Projects
      1. Capstone Projects
      2. Exploratory Data Analysis Project
      3. Real life project with Kaggle competition dataset
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