The 5 Ps of Data Science: Where a Busy Data Team Stalls
The 5 Ps of data science (Problem, People, Process, Platform, Portfolio): the question each forces, the failure it exposes, and why the order matters.
Writer
Vanshika Nigam
ETL & Data Engineer, Accenture
Gurugram, India
SQLETL pipelinesSnowflakeData analysis
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Vanshika is an application development associate at Accenture in Gurugram, where she designs and optimises data integration workflows using Informatica PowerCenter. Her day-to-day covers SQL query building, data mapping and cleansing, and tracing performance bottlenecks across systems — work that reduced data-related defects by 20% on her project. She also works with Snowflake, DBT, Control-M and StreamSets. She holds a BTech in Information Technology from PSIT College of Engineering and is a Microsoft Certified Azure Fundamentals holder.
14 articles by Vanshika
Advanced Data Analytics Course: Choosing One Beyond Dashboards
Pick an advanced data analytics course that goes past dashboards: five options compared on price and predictive depth, plus the four topics a syllabus needs.
Data Analytics Course with Python: Online Options Worth Paying For
How to choose a data analytics course with Python online: five options compared, the small Python subset analysts need, and why SQL still comes first.
Data Science Tools and Techniques: The Stack That Actually Gets Used
The data science tools working analysts actually use, what each is for, when to learn it, and the validation techniques that matter more than any library.
NumPy Tutorial for Beginners: NumPy Arrays in Python (Part 1)
NumPy tutorial for beginners: what an array is, why it ran about 20 times faster than a list here, how one stray string turns numbers into text, and more.
NumPy Indexing and Slicing: Views, Masks and Fancy Indexing (Part 2)
NumPy indexing and slicing explained: why editing a slice changes your original array, when masks and fancy indexing copy instead, and how to avoid both bugs.
NumPy Broadcasting in Python: Rules and Common Bugs (Part 3)
NumPy broadcasting explained: the two shape rules, why a (3,1) and a (4,) array give (3,4), and the silent bug that turns a vector sum into a matrix.
NumPy Aggregation, Sorting and Linear Algebra in Python (Part 4)
NumPy aggregation along axes, NaN-safe statistics, argmax, sorting and linalg.solve, plus why NumPy and pandas return different standard deviations.
Plotly Tutorial for Python: Basic Interactive Charts (Plotly Part 1)
Plotly tutorial for Python: interactive charts in one Plotly Express call, when to use Graph Objects, styling, faceting, and HTML exports 100x smaller.
Data Scientist Roadmap 2026: A Seven-Month Path from SQL to Offer
A stage-gated data scientist roadmap for 2026: SQL, statistics, modelling and communication, weighted as interviews test them, with projects and free resources.
Advanced Plotly Charts: Subplots, Animation and 3D (Plotly Part 2)
Advanced Plotly charts in Python: subplots, dual axes, animation, 3D and heatmaps, plus the axis-range mistake that silently drops points from animations.
Advanced Matplotlib Styling and Customisation in Python (Part 5)
Matplotlib styling for report-ready charts: rcParams, style sheets, colormaps, annotation and clean export, and why plt.style.use('seaborn') now fails.
Python Numeric Data Types: int, float, Decimal and When to Use Each
Python numeric data types explained: int, float, Decimal, Fraction and complex, why 0.1 + 0.2 is not 0.3, and how to handle money without rounding errors.
Data Analyst vs Data Scientist: Which Role to Target First
Data analyst vs data scientist: how daily work, skills, pay and entry routes differ, with one dataset analysed both ways and a rule for which to target first.