Greetings,

I am so excitedto learn that you have started your path to becoming aData Scientist with my course. Data Scientist is in-demand andmost satisfyingcareer, where you will solve the most interesting problems and challenges in the world. Not only, you will earn average salary of over $100,000 p.a., you will also see theimpact of your work around your,is not is amazing?

This isone of the mostcomprehensive course on any e-learning platform (including Udemy marketplace) which uses the power of Python to learn exploratory data analysis and machine learning algorithms. You will learn the skills to dive deep into the data and present solid conclusions for decision making.

Data Science Bootcamps are costly, in thousands of dollars. However,this courseis only a fraction of thecost of any such Bootcampand includesHD lecturesalong with detailedcode notebooksfor every lecture. The course also includespracticeexercises on real datafor each topic you cover, because the goal is "Learn by Doing"!

For your satisfaction, I would like to mentionfew topics that we will be learning in this course:

• Basis Python programming for Data Science

• Data Types, Comparisons Operators, if, else, elif statement, Loops, List Comprehension, Functions, Lambda Expression, Map and Filter

• NumPy

• Arrays, built-in methods, array methods and attributes, Indexing, slicing, broadcasting & boolean masking, Arithmetic Operations & Universal Functions

• Pandas

• Pandas Data Structures - Series, DataFrame, Hierarchical Indexing, Handling Missing Data, Data Wrangling - Combining, merging, joining, Groupby, Other Useful Methods and Operations, Pandas Built-in Data Visualization

• Matplotlib

• Basic Plotting & Object Oriented Approach

• Seaborn

• Distribution & CategoricalPlots, Axis Grids, Matrix Plots, Regression Plots, Controlling Figure Aesthetics

• Plotly and Cufflinks

• Interactive &Geographical plotting

• SciKit-Learn (one of the world's best machine learning Pythonlibrary) including:

• Liner Regression

• Over fitting , Under fitting Bias Variance Trade-off, saving and loading your trained Machine Learning Models

• Logistic Regression

• Confusion Matrix, True Negatives/Positives, False Negatives/Positives, Accuracy, Misclassification Rate / Error Rate, Specificity, Precision

• K Nearest Neighbour (KNN)

• Curse of Dimensionality, Model Performance

• Decision Trees

• Tree Depth, Splitting at Nodes, Entropy, Information Gain

• Random Forests

• Bootstrap, Bagging (Bootstrap Aggregation)

• K Mean Clustering

• Elbow Method

• Principle Component Analysis (PCA)

• Support Vector Machine

• Recommender Systems

• Natural Language Processing (NLP)

• Tokenization, Text Normalization, Vectorization, Bag-of-Words (BoW), Term Frequency-Inverse Document Frequency (TF-IDF), Pipeline feature........and MUCHMORE..........!

Not only the hands-on practice using tens of real data project, theory lectures are also provided to make you understand the working principle behind the Machine Learning models.

So, what are you waiting for, this is your opportunity to learn the real Data Science with a fraction of the cost of any of your undergraduate course.....!

Brief overviewof Data around us:

According to IBM, we create 2.5 Quintillion bytes of data daily and 90% of the existing data in the world today, has been created in the last two years alone. Social media, transactions records, cell phones, GPS, emails, research, medical records and much more…., the data comes from everywhere which has created a big talent gap and the industry, across the globe, is experiencing shortage of experts who can answer and resolve the challenges associated with thedata. Professionals are needed in thefield of Data Sciencewho are capable of handling and presenting the insights of the data to facilitate decision making. This is the time to get into thisfield with the knowledge and in-depth skills of data analysis and presentation.

Have Fun andGood Luck!