TEKLEARN

***At TekLearn, we believe in transforming learners into
 industry-ready professionals. Our Data Science program is
 crafted to equip students with cutting-edge skills, hands-on
 experience, and career-building opportunities. Dive deep
 into data analytics, machine learning, and AI, and prepare
 to thrive in one of the most sought-after fields of the
 modern tech era*

Data Science Batch 102

Become a Data Driven Expert & Turn Data Into Decisions. Turn Skills Into Careers and Monetization opportunities. 

Anaconda and need for environments

Setting up python and VS Code

Git & GitHub

Packages, Libraries and Existing code

Assignment 00: Tools for Data Science

What Is Data Science? The essence.

Understanding basic terminology

Types of data

Need for data science

Assignment 01: Introducing Data Science 

Python Booster I– imports, data types, functions, operations

Python Booster II– loops and control flow, scope, numpy arrays.

Mathematics Booster I– vector operations, sets, matrix operations

Mathematics Booster II– probability and random variables

Assignment 02: Covering the basics (Python) 

Types of data files  – csv, tsv, excel, images, series, dataframe

Pandas Basic I : loading different data files, head(), tail(), column subset  methods, row subset methods – loc[] and iloc[]

Pandas Basic II: select column as series and values, rename columns, remove  columns, add columns, sort values in columns, filter row by columns values, apply  multiple filters 

Pandas Basic III: string methods, the pandas loop(), type checking in pandas,  assignment using .at[idx, col], together. 

Pandas Basic IV: groupby() methods, concat(), and merge()  

Pandas Basic V: Useful pandas operations: value_counts(), sum(),  min(), max(), mean(), std(), reset_index(), transpose, and  sample() 

Pandas Basic VI: Handling null values – isna(), pd.isnull(),  pd.notnull(), fillna(), dropna(), dropna(subset=??), interpolating &  imputing missing values using sklearn.

Pandas Basic VII: Special types conversions – datetime methods and  conversions of strings, and normalization (standard and scalar).  Assignment 03

Assignment 03: Working with data and pandas 

Line Graphs 

Scatter Plots & Kernel Density (line vs continuous)

 Bar Charts & Frequency Plots 

 Histogram & KDE 

 Box Plots

 Error Plots 

2D histogram and density plots

Word Plot 

Assignment 04 Data Visualization 

Missing Plots & Heatmaps

Cluster Maps & Pie Charts 

Geospatial Visualization & Other libraries  

 Complex Visualization: Combining multiple plots  

Assignment 05: Advanced Visualization

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Data Science Batch 102

What you'll get out of this course

Your Instructor

TEKLEARN