Greetings,
I am so excited to learn that you have started your path to becoming a Data Scientist with my course. Data Scientist is in demand and most satisfying career, 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 the impact of your work around your, is not is amazing?
This is one of the most comprehensive course on any e learning platform 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 course is only a fraction of the cost of any such Bootcamp and includes HD lectures along with detailed code notebooks for every lecture. The course also includes practice exercises on real data for each topic you cover, because the goal is “Learn by Doing”!
For your satisfaction, I would like to mention few 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 and boolean masking, Arithmetic Operations and 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 and Object Oriented Approach
Seaborn
Distribution and Categorical Plots, Axis Grids, Matrix Plots, Regression Plots, Controlling Figure Aesthetics
Plotly and Cufflinks
Interactive and Geographical plotting
SciKit-Learn (one of the worlds best machine learning Python library) 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 MUCH MORE……….!
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 overview of 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 the data. Professionals are needed in the field of Data Science who are capable of handling and presenting the insights of the data to facilitate decision making. This is the time to get into this field with the knowledge and in depth skills of data analysis and presentation.
Have Fun and Good Luck!
Who this course is for:
For you, if you:
want to learn Data Science with Python
want to learn Machine Learning with Python
are tired of complicated courses and “Learn by Doing”
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SKILLS YOU WILL GAIN
NumPy for Numerical Data
Pandas for Data Analysis
Plotting with Matplotlib
K-Mean Clustering, Logistic Regression, Linear Regression
WHAT YOU WILL LEARN
Statistical Plots with Seaborn
Interactive dynamic visualizations of data using Plotly
SciKit-Learn for Machine Learning
Random Forest and Decision Trees